{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-15T17:40:05.056578Z",
     "start_time": "2020-11-15T17:40:05.050849Z"
    }
   },
   "source": [
    "<ul id=\"breadcrumb\">\n",
    "<li><a href=\"#第7章-支持向量机\">&nbsp;</a></li>\n",
    "<li><a href=\"#概念知识\">概念知识</a></li>\n",
    "<li><a href=\"#线性可分SVM\">线性可分SVM</a></li>\n",
    "<li><a href=\"#课本习题1.2\">课本习题1.2</a></li>\n",
    "<li><a href=\"#线性SVM\">线性SVM</a></li>\n",
    "<li><a href=\"#序列最小最优化SMO\">序列最小最优化SMO</a></li>\n",
    "<li><a href=\"#scikit-learn实例\">scikit-learn实例</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第7章 支持向量机"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1．支持向量机最简单的情况是线性可分支持向量机，或硬间隔支持向量机。构建它的条件是训练数据线性可分。其学习策略是最大间隔法。可以表示为凸二次规划问题，其原始最优化问题为\n",
    "\n",
    "$$\\min _{w, b} \\frac{1}{2}\\|w\\|^{2}$$\n",
    "\n",
    "$$s.t. \\quad y_{i}\\left(w \\cdot x_{i}+b\\right)-1 \\geqslant 0, \\quad i=1,2, \\cdots, N$$\n",
    "\n",
    "求得最优化问题的解为$w^*$，$b^*$，得到线性可分支持向量机，分离超平面是\n",
    "\n",
    "$$w^{*} \\cdot x+b^{*}=0$$\n",
    "\n",
    "分类决策函数是\n",
    "\n",
    "$$f(x)=\\operatorname{sign}\\left(w^{*} \\cdot x+b^{*}\\right)$$\n",
    "\n",
    "最大间隔法中，函数间隔与几何间隔是重要的概念。\n",
    "\n",
    "\n",
    "\n",
    "2．现实中训练数据是线性可分的情形较少，训练数据往往是近似线性可分的，这时使用线性支持向量机，或软间隔支持向量机。线性支持向量机是最基本的支持向量机。\n",
    "\n",
    "对于噪声或例外，通过引入松弛变量$\\xi_{\\mathrm{i}}$，使其“可分”，得到线性支持向量机学习的凸二次规划问题，其原始最优化问题是\n",
    "\n",
    "$$\\min _{w, b, \\xi} \\frac{1}{2}\\|w\\|^{2}+C \\sum_{i=1}^{N} \\xi_{i}$$\n",
    "\n",
    "$$s.t. \\quad y_{i}\\left(w \\cdot x_{i}+b\\right) \\geqslant 1-\\xi_{i}, \\quad i=1,2, \\cdots, N$$\n",
    "\n",
    "$$\\xi_{i} \\geqslant 0, \\quad i=1,2, \\cdots, N$$\n",
    "\n",
    "求解原始最优化问题的解$w^*$和$b^*$，得到线性支持向量机，其分离超平面为\n",
    "\n",
    "$$w^{*} \\cdot x+b^{*}=0$$\n",
    "\n",
    "分类决策函数为\n",
    "\n",
    "$$f(x)=\\operatorname{sign}\\left(w^{*} \\cdot x+b^{*}\\right)$$\n",
    "\n",
    "线性可分支持向量机的解$w^*$唯一但$b^*$不唯一。对偶问题是\n",
    "\n",
    "$$\\min _{\\alpha} \\frac{1}{2} \\sum_{i=1}^{N} \\sum_{j=1}^{N} \\alpha_{i} \\alpha_{j} y_{i} y_{j}\\left(x_{i} \\cdot x_{j}\\right)-\\sum_{i=1}^{N} \\alpha_{i}$$\n",
    "\n",
    "$$s.t. \\quad \\sum_{i=1}^{N} \\alpha_{i} y_{i}=0$$\n",
    "\n",
    "$$0 \\leqslant \\alpha_{i} \\leqslant C, \\quad i=1,2, \\cdots, N$$\n",
    "\n",
    "线性支持向量机的对偶学习算法，首先求解对偶问题得到最优解$\\alpha^*$，然后求原始问题最优解$w^*$和$b^*$，得出分离超平面和分类决策函数。\n",
    "\n",
    "对偶问题的解$\\alpha^*$中满$\\alpha_{i}^{*}>0$的实例点$x_i$称为支持向量。支持向量可在间隔边界上，也可在间隔边界与分离超平面之间，或者在分离超平面误分一侧。最优分离超平面由支持向量完全决定。\n",
    "\n",
    "线性支持向量机学习等价于最小化二阶范数正则化的合页函数\n",
    "\n",
    "$$\\sum_{i=1}^{N}\\left[1-y_{i}\\left(w \\cdot x_{i}+b\\right)\\right]_{+}+\\lambda\\|w\\|^{2}$$\n",
    "\n",
    "3．非线性支持向量机\n",
    "\n",
    "对于输入空间中的非线性分类问题，可以通过非线性变换将它转化为某个高维特征空间中的线性分类问题，在高维特征空间中学习线性支持向量机。由于在线性支持向量机学习的对偶问题里，目标函数和分类决策函数都只涉及实例与实例之间的内积，所以不需要显式地指定非线性变换，而是用核函数来替换当中的内积。核函数表示，通过一个非线性转换后的两个实例间的内积。具体地，$K(x,z)$是一个核函数，或正定核，意味着存在一个从输入空间x到特征空间的映射$\\mathcal{X} \\rightarrow \\mathcal{H}$，对任意$\\mathcal{X}$，有\n",
    "\n",
    "$$K(x, z)=\\phi(x) \\cdot \\phi(z)$$\n",
    "\n",
    "对称函数$K(x,z)$为正定核的充要条件如下：对任意$$\\mathrm{x}_{\\mathrm{i}} \\in \\mathcal{X}, \\quad \\mathrm{i}=1,2, \\ldots, \\mathrm{m}$$，任意正整数$m$，对称函数$K(x,z)$对应的Gram矩阵是半正定的。\n",
    "\n",
    "所以，在线性支持向量机学习的对偶问题中，用核函数$K(x,z)$替代内积，求解得到的就是非线性支持向量机\n",
    "\n",
    "$$f(x)=\\operatorname{sign}\\left(\\sum_{i=1}^{N} \\alpha_{i}^{*} y_{i} K\\left(x, x_{i}\\right)+b^{*}\\right)$$\n",
    "\n",
    "4．SMO算法\n",
    "\n",
    "SMO算法是支持向量机学习的一种快速算法，其特点是不断地将原二次规划问题分解为只有两个变量的二次规划子问题，并对子问题进行解析求解，直到所有变量满足KKT条件为止。这样通过启发式的方法得到原二次规划问题的最优解。因为子问题有解析解，所以每次计算子问题都很快，虽然计算子问题次数很多，但在总体上还是高效的。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 概念知识\n",
    "----\n",
    "\n",
    "### 超平面、分离超平面、决策边界/间隔边界、间隔、支持向量、支持向量机等的可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-27T01:26:49.577240Z",
     "start_time": "2020-11-27T01:26:49.271524Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "class1 = np.array([[1, 1], [1, 3], [2, 1], [1, 2], [2, 2]])\n",
    "class2 = np.array([[4, 4], [5, 5], [5, 4], [5, 3], [4, 5], [6, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-27T01:26:51.654932Z",
     "start_time": "2020-11-27T01:26:51.258834Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.5, 'support vector')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QrVtn77uQlCRJqkyciixJkiRJymsGW0mSJElSXjPYSpIkSZLymsFWkiRJkpTXDLaSJEmSpLxmsJUkSZIk5TWDrSRJkiQprxlsJUmSJEl5zWArSZIkScpr1dIuQJKUG5tvnnYFkiRJ6TDYSlIF8e23aVcgSZKUDqciS5IkSZLymsFWkiRJkpTXDLaSJEmSpLzmObaSVEEMH56936NHenVIkiRtbAZbSaogDjssez/G9OqQJEna2JyKLEmSJEnKawZbSZIkSVJeM9hKkiRJkvKawVaSJEmSlNcMtpIkSZKkvGawlSRJkiTlNYOtJEmSJCmvGWwlSZIkSXnNYCtJkiRJymvV0i5AkpQbBQVpVyBJkpQOg60kVRCFhWlXIEmSlA6nIkuSJEmS8prBVpIkSZKU1wy2kiRJkqS85jm2klRB3H139v7JJ6dXhyRJ0sZmsJWkCqJv3+x9g60kSapMnIosSZIkScprBltJkiRJUl4z2EqSJEmS8prBVpIkSZKU1wy2kiRJkqS8ZrCVJEmSJOU1g60kSZIkKa8ZbCVJkiRJec1gK0mSJEnKa9XSLkCSlBvdu6ddgSRJUjoMtpJUQQwfnnYFkiRJ6XAqsiRJkiQprxlsJUmSJEl5zWArSZIkScprnmMrSRXE5ZeXfl+SJKmiCzHGtGsoVQihsKCgoKCwsDDtUiQpL4SQvV9Of7VLkiSVFH55l7XjVGRJkiRJUl4z2EqSJEmS8prBVpIkSZKU1wy2kiRJkqS8ZrCVJEmSJOU1g60kSZIkKa95HVtJkjamh46CT59dt+e07ATHPbZh6tG68fsnSeVSmUdsQwgHhRDiam435aJISZIqjHUNRev7HG0Yfv8kqVzKxYhtw8z2auDTlR77IAfHlyRJkiRptXIZbB+MMX6Yg+NJktbDn/+cdgWSJEnpyEWwbZTZTs/BsVQRxAjLlkE1T+GWNqa77067AkmSpHTkYlXkhsAS4LgQwlchhMUhhIkhhK45OLby0aWXwuGHw8KFaVciSZIkqRLI1VTk6sDfgNuBkLn/VAhh9xjjx2t6cgihcDUP7ZyD2rSx9esHV16Z3D/sMBg2DGrXTrcmSZIkSRVaLkZsvwBGAvvHGP8ZY7wGOJUkNJ+fg+MrnyxenL3/3HPQrRvMm5dePZIkSZIqvDKP2MYYrwWuXal7dGa721o8v1Vp/ZmR3IKyVaeNrl8/qFEjmY4M8OKL0KULjBoF9eunWppU0Z18cva+59tKkqTKZEOt7vNTZttojXupYrrkEthkE/i//0va48ZBp07wzDPQoEG6tUkV2D33ZO8bbCVJUmVSpqnIIYTGIYT3Qwg3rfTQFpntN2U5vvLY3/4GN96Ybb/xBhx8MMyenV5NkiRJkiqksp5jOxtoChwdQqhZov93me2oMh5f+ezss+H227PtwkI46CCYOTO9miRJkiRVOGUKtjHGCFwFbA68GEI4K4RwI/APYBJwR9lLVF477bRkfmQISXvCBGjfHr77LtWyJEmSJFUcZV4VOcZ4M3ASUA/oD/weGAC0izHOL+vxVQGcdBIMHAhVMj9uH3wA7drB1KmpliVJqWjZaeM8RxuG3z9JKpdCMuha/oQQCgsKCgoKC1d3mVvlnUcegd69YdmypL399vDCC9C8ebp1SRVE0cQIgHL6q12SJKmk8Mu7rJ1cXMdWWju/+x38979QLbMY9+TJycjtlCnp1iVJkiQprxlstXEdeSQ8/nhyrVuAL76AAw+ETz9NtSxJkiRJ+ctgq43vsMPgySeTa90CfPNNMnL70Ufp1iVJkiQpLxlslY4uXWDkSKhVK2lPm5aE2/ffT7cuSZIkSXmnWtoFqBLr2BGefhq6dYOff4YZM5JLAY0eDXvtlXZ1Ut657LK0K5AkSUqHqyIrfePGQdeu8NNPSbthQ3j2WWjdOt26JEmSJG1IroqsCmT//ZNR2gYNkvacOclo7muvpVuXJEmSpLxgsFX5sM8+8Pzz0KhR0v7xR+jUCV5+Od26JEmSJJV7BluVHwUF8OKL8KtfJe1585JFpl54IdWyJEmSJJVvBluVL3vskYTbZs2S9vz5yeJS//tfqmVJ+aBHj+xNkiSpMnHxKJVPn3wCHTrA1KlJu0YNePxx6N493bqkciyUWH6hnP5qlyRJKsnFo1TB7bgjjB0LzZsn7cWLoVcveOKJdOuSJEmSVO4YbFV+bb89vPQSbLdd0l6yBI46Ch59NN26JEmSJJUrBluVb9tsk4zctmyZtJctg2OPhQcfTLcuSZIkSeWGwVbl31ZbJeF2l12S9vLl8Ic/wP33p1uXJEmSpHLBYKv8sPnmyWrJe+yRtGOEP/0J7rwz1bIkSZIkpc9gq/zRtCmMGQO//nW279RT4ZZb0qtJkiRJUuoMtsovjRvD88/DPvtk+/7yF+jfP72aJEmSJKXKYKv807AhPPss7Ldftu/88+Gqq9KrSZIkSVJqDLbKT5tuCs88AwcemO27+GK47LLk/FtJkiRJlUa1tAuQ1lu9ejBqFBx+eDI9GeAf/4BFi+CaayCEdOuTNrK77kq7AkmSpHQYbJXf6tSB4cOhV69kBBfg2mth8WK4/nrDrSqVk09OuwJJkqR0OBVZ+a9WLRg2DA47LNt3441w5pnJNW8lSZIkVWgGW1UMm2wCjz0GRx6Z7bv9dujb13ArSZIkVXAGW1UcNWrAI4/A736X7bv3XvjjH2HZsvTqkiRJkrRBeY6tKpZq1eDBB5OQ+8ADSd9//pOcc/vAA8njUgXVqlX2fmFhenVIkiRtbP6Vr4qnalUYMCAJt/fem/QNHgxLlsDDD0P16unWJ20gb7+ddgWSJEnpcCqyKqYqVZJrn5x2WrZvyBD47W+TywFJkiRJqjAMtqq4qlSB226Ds8/O9j31FBxxBCxYkF5dkiRJknLKYKuKLQS44Qb4v//L9j39dHJpoPnz06tLkiRJUs4YbFXxhQDXXAOXXprtGz0aDj0U5s1Lry5JkiRJOWGwVeUQAlxxBVx5ZbZv7Fjo0gV+/DG9uiRJkiSVmcFWlctFF8F112Xb48bBIYfAnDnp1SRJkiSpTAy2qnzOPx9uuinbHj8eOnaEWbPSq0nSLwohcMIJJ2z0173vvvto3779Rn9dSZK09gy2qpz+8he4445s+5134KCDYMaM9GqStEYDBgzgpJNO2iivtWDBAt5//31uuOEGzjrrrI3ymlI+GzhwIDeV/NA4T1WU9yFVRiHGmHYNpQohFBYUFBQUFhamXYoqsvvvh5NOgqJ/B7vumiwstfnm6dYlrYcQsvfL6a/2vPHII49w7LHHFrfbtWvHiy++mF5BUjnXvn17vvjiC7744ou0SymTivI+pDwSfnmXtVMtVweS8tIf/wg1asDxx8Py5fDBB9C+PbzwAmy5ZdrVSevkqafSrqDiaN++PU8//TQAXbt2TbkaSZL0S5yKLPXuDQ8/DFWrJu1PPoEDD4Qvv0y3Lmkd9eiRva2NE044gRACQ4YMoXnz5jRr1oznnnuOU045hTp16tCmTRtmzpxZvP9DDz1Eq1atqFu3Ls2aNePII4/k888/X+W4RefCLl26lP79+7Pnnnuy3377rbLf2LFjadOmDXXr1mX33Xdn4MCB/Pa3v6VmzZqljo6u6Rzbosc+/vhjunTpQt26ddlqq634xz/+sXZfjJU0a9aMLl260KVLl/V6viq3N954g86dO/OrX/2KevXq0aZNG0aMGLHCPu3bt6dFixarPPekk05aoT+EwB/+8AcuuOACmjZtSq1atejUqRPvv//+Cs9b2/0A3nvvPbp37069evWoX78+PXv25IMPPlhlvxYtWhSfX37fffexzz77sPXWWxc/PnDgQEIIhBAYO3YsX375ZXE7hHUfhJkzZw41a9bklFNOWeWxdu3ase2221JypuGkSZPo1asXjRo1YtNNN6Vdu3a8/PLLqzz3zTffpEuXLtSrV48mTZpwyCGH8Oqrr673+8jV109SbhlsJYBjjoFHH4Xq1ZP2558n4baUP9qliqZfv36cdtppTJ8+nR49ejB16lSOO+44xo8fz+DBgwF44IEH6N27Nw0aNOBf//oXZ555JmPHjqVHjx4sW7ZslWMuX76cww47jCuuuIJdd92Vzp07r/D4hx9+SOfOnalatSrXXnst22yzDSeeeCINGjTg5ptvLvUP/l/y1VdfceCBB7LFFltwzTXXUL9+fS677DKGDRu2Xl8XaX189dVXHHzwwXz++ef8/e9/57rrrqNevXr07NmTt956a72O+dhjj/HAAw9w7rnnct555/H666/Ttm3bVabLrs1+r776Kvvuuy9vv/02f//73zn//PN59dVXadOmDW+//Xapr9+3b19OO+00ttxyS3r27Fnc37ZtWwYMGMCAAQPYaaedaNy4cXF7wIAB6/w+GzZsSPfu3RkyZAhLliwp7v/mm294+eWX6d27d3HQfOedd2jTpg2ff/45F198Mf369ePnn3+mffv2K3ww9sILL9C2bVsmTZrEhRdeyAUXXMBXX31Fu3btGDt27Dq/j1x+/STlWIyxXN6AwoKCgihtVMOHx1ijRozJKYoxbrlljJ98knZV0gZx/PHHRyCOHz8+xhjj5ptvHhs1ahQXLFgQP/744wjEyy67LMYY4wUXXBB79eoVFy5cWPz8a6+9NgJx0qRJKxwXiLVr14577LFHnD59eqmvffHFF0cgfvnllzHGGOfPnx9r164de/Xqtdp6gXj88cev9jEg9uvXr7jv3XffjUA844wzfvFrsSZAbNeuXZmOocpjyJAhEYjDhg0r7ps7d27s27dvfO6554r72rVrF7fZZptVnv+nP/1phX4gVqtWLX788cfFfSNGjIhAPPPMM9d5v1133TXWr18/fvPNN8V9kydPjrVr146tW7deoZZtttkm1q5dO2611Vbxk1/4v3B172ddDRs2LAJxxIgRxX3/+te/IhA/+uij4r727dvHzTffPE6ZMiVOmzYtTps2LX7yySdxk002iT169Cjer2XLlrFBgwZx2rRpxX2TJ0+OQDzmmGPW+X1sqK+fVInlLD96jq1UUvfuyYmKPXvCwoUwdWoycvvCC7DLLmlXJ20Qv/71rwGoUaMG2223HTVr1qRGjRor7HP11VcD8PXXX/PWW2/x+uuv88ADDwAwY8YMdt111xX2X7x4MY8//jhNmzYt9TUXLFgAQIMGDQCoVasWNWvWXGGa4bpq1qwZ559/fnF7jz32AOCnn35a72NK62rvvfemZs2a/POf/yTGSKtWrdh6662588471/uYbdu2Zccddyxud+vWjbp16/Laa6+t036fffYZH3zwAX/84x/ZssQ6Ettttx2HH344gwcPZurUqSs8Nn/+fEaOHEnLli3Xu/51ceihh9KkSRMGDx5Mt27dABg8eDBt2rRhp512Kq7p5ZdfZtmyZWy77barHOPdd98F4NNPP+XTTz/lpJNOolmzZsWPb7fdduv1uyYfvn5SZeZUZGllnTvDyJFQu3bS/u47aNcOJk5Mty7pF2yxRfa2LqpVq1bq/ZLGjRvHnnvuSfPmzTnxxBN54403aN269WqPefjhh6/xD7nu3bsDcOmllzJ58mSuu+46Zs+eTadOndat+BJatWrFJptsUtyuUsX/4rTxNW/enBEjRlCnTh369OlD8+bN2XLLLbnoootWmF67Oj/++OMqfSVDWZEmTZrwww8/rNN+06dPB2CrrbZaZb+icz+L9inSqlWrjXod5+rVq3PMMcfw5JNPMn/+fD755BPefvtt+vTpU7zPnDlzWLZsGZ07d+bpp59e5TZw4EAg+dANWCFolkU+fP2kyswRW6k0HTrA009Dt24wbx7MnJlc5/a55yAzuiWVN9OmbZjjzps3j8MOO4zNN9+cCRMmFI+EDhw4cJUFcYrUrVt3jcds3rw5m2++Obfddhs333wzkCyac/LJJ693nU2aNFnv50q51LFjRzp27EiMkcmTJzNw4ECuuuoqatWqxcUXX7zG506YMGGVvm+++WaF9vLly5kxYwa77777Ou232WablbofJLMxSu5T5Jf+LW8Iffr04fbbb2f48OF8+OGHxWG3SMOGDalatSpVq1ZdZYG3Tz75pHiWRgTKafYAACAASURBVNGMkalTp67yGpdccgkzZszgrrvuWuu68uXrJ1VWfpwtrc6BB8Kzz0L9+kl71qwk8L75Zrp1SRvZxx9/zOzZs+nZs2dxqF20aBGDBg1a72MeddRRdOvWje+++46XXnqJr7/+mnvuucdRVuW9Sy65hK222orp06cTQmCHHXagX79+bLrppowfP754v7p16zJz5swVRnGHDBnCxx9/vMoxX331VSZNmlTcLhrNbNu27Trtt8MOO7Drrrvy+OOP8+233xbv98UXX/Dkk0/SqlWr9R7dbNKkCT/99FOZTico0qZNG3bccUcefPBBBg8eXDw9uUjt2rU54IADGDNmDF999VVx/7x582jbti1//vOfAWjZsiUtW7Zk6NChxaO3ALNmzeLmm28u9UOENb2PDfn1k1R2jthKa7LvvjB6NHTqBD/8kNwOPjgZzS3l8iVSRbT99ttTp04d7r33Xpo0acL8+fMZNGhQ8aV+is6XXRebbropw4YNY4sttmDLLbfkq6++olGjRrRq1Wq15+VK+aBjx45cddVVdOnSheOOO4569erxzDPPMHfuXDp06FC830EHHcTIkSP5+9//zjnnnMO4ceM4/fTT2WWXXZg/f/4Kx6xbty6HHHIIf/nLX5g3bx433XQTDRs25Oyzz17n/e688046derE3nvvzemnn87y5cu59dZbCSGU6TzgHj168Pjjj3PiiSfStm1bpk2bxs8//8w///nP9Tpenz59uOSSS4DsOf4l3XDDDRxwwAG0bt2a0047jcaNGzNo0CBmzZrF/fffX7zfHXfcwaGHHkrr1q059dRTqVGjBvfddx8LFizgyiuvXOf3saG+fpJyIJcrUeXyhqsiqzx5++0YGzfOrpZcp06MY8emXZW0gqIfT1i7/YtWRS6yzTbbFK/+O2XKlBVWRR4zZkzcZ599Yq1atWKzZs3iaaedFh9//PEIxNNOO22lOla/enGR8ePHx2rVqsWtt9461qpVq3hV42rVqsXbbrttNe9vzasil/bY2tTyS3BVZK2jZ599Nh5yyCGxadOmsWbNmnGXXXaJ119//Qr7LFq0KJ555pmxadOmsXbt2rFt27Zx7Nixpa6KfPzxx8d+/frFzTbbLNasWTMecsgh8f3331/heGu7X4wxvv3227Fr166xTp06sW7durFHjx5x4sSJq+xX8nfC2ujfv3/cfvvtY7Vq1WLjxo3j5ZdfvtbPXdmUKVNiCCE2aNBghdXYS5o4cWLs2bNn3HTTTWO9evXiAQccEEePHr3KfuPHj4+dOnWKderUiU2aNIldunSJb7311nq/jw319ZMqqZzlxxBzMGVkQwghFBYUFBQUFhamXYqUmDgxGa0tms5UqxYMHw4dO6Zbl5SRubwjkMTb8mrGjBnstNNO3H333Rx11FFA8iHr7NmzOeGEExg3bhyzZ89OuUopsXz5ciZOnMjEiRPp2rUrjRs33qivH0Lg+OOPL14Qqaz7SVI5E355l7XjVGRpbe2xB7z4YhJkp02DBQuSywM98QSstHiFpNWbO3cuP/74IzfeeCOTJ0+mSZMmLFu2jMmTJ/PKK6+w8847p12iKrEYI5MmTWLMmDG8+OKLjB07llmzZgFwwQUXlDotVpKUPoOttC522QXGjk0Wkfrmm+Rat4cfDkOGQI8eaVcn5YWWLVvy5JNPctNNN3H99dczZ84catWqRYsWLejbty/nnXde2iWqkpk1axZPP/00I0aM4IUXXmDmzJkrPL711lvToUOH4kWJJEnlj1ORpfUxZUpy+Z8vv0za1arBf/8LvXqlW5cqtXyZiiylLcbIhx9+yPDhwxkxYgSvvvoqy5cvL358iy224KCDDqJ9+/YcdNBBbLfddoSQs9lykqQspyJLqdp2W3jppSTcfv45LF0KRx8NDz4Iv/td2tVJklayePFixo4dy4gRIxg+fDhTpkwpfqx69ep06NCBHj160LlzZ3bccUeDrCTlGYOttL6aN0/CbYcO8MknsGwZHHccLF4Mf/hD2tVJUqU3c+ZMRo0axfDhw3n22Wf56aefih9r0qQJ3bp1o3v37nTq1In6RdcslyTlJYOtVBZbbpmcc9uxI3zwASxfDiecAEuWwJ/+lHZ1qmTeeivtCqT0zZw5k0ceeYRHHnmE1157jZKnXO2xxx50796dHj16sM8++1C1atUUK5Uk5ZLBViqrZs2S1ZIPPhgmTEhObjzppGTk9tRT065OlUirVmlXIKVjwYIFPPXUUwwaNIhnnnmGZcuWAVCjRg0OOuggevToQbdu3WjRokW6hUqSNhiDrZQLv/oVvPACdOoEb7+d9J12WhJu//KXdGuTpApo+fLljB07lkGDBjFkyJDiacZVq1alW7du9O7dm+7du1O3bt2UK5UkbQwGWylXGjeG559Prmn7xhtJ39lnw6JF8Le/pVubJFUQkyZNYtCgQTz00EN88803xf177703ffr04ZhjjqFp06YpVihJSoPBVsqlBg3g2Wfh0ENh3Lik7//+Lwm3l1ySbm1SOXfrrbdSq1YtTjrppLRLUTnz3XffMXjwYAYNGsQ777xT3N+iRQt69+7Ncccdx84775xihZKktHkdW2lDmDcPevRIzr0tcvHF8I9/rHixUSmH8vk6tvPmzaN+/fo0bNiQWbNmpV2OyoEYI6+88gq33HILTzzxRPF5sw0aNODoo4+md+/e7L///lSpUiXlSiVJZeB1bKVyrW5dGDkSevaE555L+q68Mjnn9p//NNxKK5k5cyYxRs+HFAsWLGDw4MHccsstvPfeewBUq1aNnj170rt3b7p160bNmjVTrlKSVN4YbKUNpXZteOopOPJIGDUq6bvuumRa8o03Gm6lEr7//nsgubaoKqevvvqKO+64g3vuuad41L5p06b07duXU045hS222CLlCiVJ5ZnBVtqQataEoUPhmGPgySeTvptvTkZub7sNnEInAQbbyirGyMsvv1w83Xj58uUAtG7dmrPOOoujjz6aTTbZJOUqJUn5wGArbWibbAKPPQa//z0MGZL03XFHEm7vuguqVk23PqkcKBqhM9hWDgsWLODhhx/mlltuYcKECUAy3fiYY47hrLPOok2bNgRntUiS1oHBVtoYqleHwYOhRg14+OGk7777knA7YIDhVpXe3LlzAdh0001TrkQb0vfff8+NN97InXfeyezZs4FkuvEpp5xC3759nW4sSVpvBltpY6lWDR54IAm3AwcmfYMGwZIlSX/16qmWJ6WpaubDnaKpqKpYZs6cSf/+/bn99tv5+eefAacbS5Jyy2ArbUxVqyYjtdWrwz33JH2PPJKM3BaN6EqVULVqyX9HS5cuTbkS5dKMGTP417/+xb///W/mz58PwKGHHspFF13Evvvu63RjSVLOuHKNtLFVqQJ33gmnn57tGzoUfvvbZMVkqRIy2FYs06dP57zzzmPbbbelf//+zJ8/n27dujF+/HhGjhzJfvvtZ6iVJOWUwVZKQ5UqcOutcM452b7hw5Pr3i5YkF5dUkoMthXDd999x7nnnsu2227L9ddfz/z58+nRowdvvvkmI0aMYO+99067RElSBWWwldISAvTvDxdckO175hno0QMy56BJlYXBNr9NmzaNv/71r2y77bbccMMNLFiwgMMOO4y33nqLp556itatW6ddoiSpgvMcWylNIcBVVyXn1l5xRdL3/PNw6KEwYgTUq5dufcorU6emXcH6M9jmp5kzZ3L11Vdz5513snDhQgB69uzJpZdeyq9//euUq5MkVSYGWyltIcDllyfh9qKLkr6XXoLOneHpp8HLn2gt5fOVUoqC7bJly1KuRGtj8eLF3HrrrfTr16/4Uk1HHHEEl156KXvttVfK1UmSKiOnIkvlxYUXJlOTi7z2GhxyCMyZk15N0kbiiG1+iDHy1FNPsdtuu3Heeecxd+5cOnfuzDvvvMPQoUMNtZKk1BhspfLk3HPhlluy7TffhA4d4Pvv06tJ2ggMtuXfhAkTOOSQQzj88MP57LPP2HnnnRk1ahTPPPOMgVaSlDqDrVTenHkm3HVXtv3uu0m4nTEjvZqUF779NnvLN/Uy55PPmjUr5Uq0shkzZnDKKafw61//mueff56GDRtyyy23MGHCBLp27Zp2eZIkAQZbqXw6+WS4//7k/FuAiROhfXuYNi3VslS+bbll9pZvdthhBwA+/fRTYowpVyOARYsW0b9/f1q2bMldd91FCIGzzjqLzz77jDPPPJPq1aunXaIkScUMtlJ5deKJ8MADyTVvAT78ENq1g2++SbcuaQNo2rQp9evX54cffuB7p96nKsbIsGHD2G233Tj//PP58ccf6dq1KxMnTuTmm2+mUaNGaZcoSdIqDLZSeda7NwweDFWrJu1PP4UDD4Qvvki1LCnXQgjsuOOOQDJqq3R8+OGHdOzYkSOOOILJkyezyy67MGrUKEaNGsUuu+ySdnmSJK2WwVYq744+GoYMgaJpf1OmJCO3kyenW5eUYy1btgTgk08+SbmSymfx4sVceeWV7LXXXowZM4ZGjRpx66238t5773kerSQpLxhspXzQsyc88URyrVuAr75Kwu3HH6dbl5RDRSO2BtuN66233mLvvffmkksuYfHixfzpT3/i008/5YwzzvA8WklS3jDYSvmiWzcYPhxq1kzaU6cm4faDD9KtS8qRohFbpyJvHPPnz+f888+nTZs2TJgwge22247Ro0dz7733eh6tJCnvGGylfNKpE4wcCbVrJ+3p05PVkidMSLUsKRccsd14xowZw5577kn//v0BOPfcc5k4cSIdO3ZMuTJJktaPwVbKNx06wDPPQN26SXvmTDjoIHj77XTrksqo5Ijt8uXLU66mYvrhhx84+eST6dChA5MnT2b33Xfntddeo3///tQu+sBMkqQ8ZLCV8tEBB8Bzz8Gmmybt2bOhY0cYPz7duqQyaNCgAU2bNmXBggV8/vnnaZdT4Tz55JPstttu3HPPPVSvXp1//OMfFBYWss8++6RdmiRJZWawlfLVb34Do0dDw4ZJ+4cf4OCDYdy4dOuSyqBdu3YA/O9//0u5kopj+vTpHHPMMfTs2ZNvv/2Wfffdl3fffZdLLrmEGkUL0kmSlOcMtlI+a90aXngBmjRJ2j/9BJ07w4svplqWtL4OPfRQAEaNGpVyJRVD0Sjto48+Su3atbn55pt5+eWX2XXXXdMuTZKknDLYSvlur71gzBjYbLOk/fPPcOihyWiuKpUYs7d81aVLFwBeeOEFFixYkHI1+WvBggWcccYZ9OzZk1mzZnHIIYcwadIkzjrrLKpWrZp2eZIk5ZzBVqoIdt89GaXdfPOkvWABdO8OTz+dalnSumrWrBmtWrVi4cKFvPjii8QY+f3vf88FF1yQdml544MPPqBNmzbcfvvtVK9enRtvvJFnnnmGFi1apF2aJEkbjMFWqih23hleegm23jppL1oEPXvCU0+lW5e0jkpOR54xYwaDBw9mwIABKVdV/sUYueeee2jdujUTJ06kZcuWvP7665x99tlUqeJ/95Kkis3/6aSKZIcdYOxYKBqZWbwYjjwShgxJtSxpXRQF25EjR/LTTz8BUKdOnTRLKhcWLFjAiBEjWLZs2SqP/fDDDxxzzDGcfPLJLFiwgOOPP57CwkIKCgpSqFSSpI3PYCtVNNtum4Tb7bdP2kuXwu9+Bw8/nG5d2uAKC7O3fPLDDz9w4YUX8sYbbwCw995707hxY6ZMmcL7778PQN2i6zZXYmeddRY9evRg2LBhK/S/+uqr7LXXXjz22GPUrVuXBx98kIEDB1KvXr2UKpUkaeMz2EoVUfPmybTknXZK2suWQZ8+8J//pFuXNqjWrbO3fDJ+/HiuueYaDjjgAAYMGEDVqlWLF5F6/vnnAYPttGnT+M9//kMIgb322guAZcuWcdVVV3HggQfy5Zdf0rp1a959912OO+64lKuVJGnjM9hKFdUWWyQjt7vtlrSXL4cTT4R77023LmklHTt25KyzzmLJkiX88Y9/5JxzzqFz584AjBkzBnAq8q233sqSJUvo1asX22+/PVOnTuXggw/m4osvZtmyZZx//vmMGzeO7YtmakiSVMmEWE6vCxFCKCwoKCgozLc5dVJ5M3MmHHIIvPdetu+22+D009OrSRtECNn75fRX+xrdc889nH766SxZsoSOHTvy2muvMX/+fACOOOIIhg4dmnKF6Zg3bx7Nmzdnzpw5vPbaa8yZM4c+ffowa9YsmjZtyqBBg+jUqVPaZUqStD7CL++ydhyxlSq6X/0KXngBWrXK9p1xBtx4Y3o1SaX485//zOjRo2nSpAnPP/881atXL36sMk9FHjBgAHPmzGG//fbjueeeo1u3bsyaNYvOnTszYcIEQ60kSRhspcqhUSMYPRp+85ts3znnwD//mV5NUikOPPBA3nzzTfbYYw/mzp1b3F+zZs0Uq0rPsmXLuDHzIdSSJUu49NJLAejXrx+jRo2iQYMGTJ06lfI6+0qSpI2lWq4PGEI4H7gO+E+M8YRcH1/SemrQAJ59Fg49FF55Jem74ILkkkCZP5al8qBFixaMGzeOPn368OSTTwLw7bffplxVDj10FHz67Frt+sQHS5gyZQHVqwbefPNN6tevzxlnnMEPP/zAAQccQGFhIYsWLeKJJ56gZ8+eG7hwAev0/SvWshMc99iGqUdrz++dVKHlNNiGEHYC/pHLY0rKoXr14JlnoEcPyCzKw2WXJeG2X78VT9KUUlSvXj2GDh3Ksccey6OPPlq8EnCFsJZ/WMcYueD5hQAsWRapXr06P/74I1dfffUK++25557sVLQCuja8dQ1G6/sc5Z7fO6lCy1mwDSFUAQYAi4HKOWdMygd16sCIEXDEEckILsBVV8GiRXDddYZblRtVqlThv//9Lw8//DBVq1ZNu5yN7oUvlvHZ7OwU4yVLllC/fn3atGnDvvvuy7777kubNm1o2LBhilVKklQ+5HLE9q/AvkBf4K4cHldSrtWuDU8+Cb/9LYwcmfT175+M3N50k+FW5UplDLUAezatwnYNA5vVqcIJe1Vjv2veYpdddqm0Xw9JktYkJ8E2hLAj0A+4F3DOhpQPataEoUPhmGNg2LCk75ZbknB7++1QxbXlpDT9qk4VJp9VL9ux++7pFSNJUjlX5mCbmYJ8P/A9cC7QqKzHlLSR1KgBjz4KvXsnW4A770zC7d13gyNDeaVRI5g9O/ttBVi4EN5+O7l/2GHJ5xkAL70E330Hu+6arCsG8OWXMHUqbLEFtG2b9M2fn8xc32STFa8YNX48LF0KBxwAm2+e9E2cCB9+CM2bw1ZbJX3ffw+ffAK1aiWndhd58slk9vvee0PRVX0mTYK5c5P8tuuuSd/UqTBuHDRuDEWnkeb9e5rUk3Fft6Fxrdns1OSz5D0t3YSPPtyBfbYppOe+T6/mOyxJklYnlPUSASGEvwI3AF1ijP8LIbQAprCWqyKHEApX89DOBQUFtQsLV/ewpJxZuhROOAEeeijb17s3DBgA1XK+eLo2kMaNk2Cr/NWIWcw6YxtoXMqHSpfPXbVPG9/lm67n8/z+pc7vnVQe5ez8tzL9xRpC2AG4ChgKvBdCaAb8KvNwrUx7doxxcdnKlLRBVasG//lPMsw0cGDS9+CDycjtgw9mh59Uru2xB4wdm9w/6qhk++23yYgnwC67ZEcyX3st2W6zDeyzT3L/sRJXtNh332T7/ffw6acrHrPkvptsAgUFKx6z5L5jxiTHKHnMkvt27gz166/+9cePh2XLKth7en0py2Ly3+9Ruz4B3yzl2x83Zxxt2XuTt6ChpwFIkrSuyjoU0xaoBfTK3Eo6OnM7CHhxdQeIMbYqrT8zkltQxvokra2qVeG++5K/6u/KrP/26KOwZAk88kgyv1Xl2osvJtNsFy5MpiWrnLq8cfb+Qz/Dj8uy7f+r5+JtkiSth7J+LPwc0HWl2wmZx0Zn2hPK+BqSNpYqVeCOO+DMM7N9TzwBvXolaUnlXu3ahtq8MeBn+KxEqL3UUCtJ0voq04htjHEqMLVkX+YcW4CpMcZnynJ8SSkIAW6+ORm57d8/6Rs5Eg4/PFk9uVatdOvTag0fnr1fclEjlUN3zYPvlif3qwMXGGolSSoLV4WRtKoQ4LrrkunHV1+d9D37LHTrlqSnOnXSrU+lOuyw7P0yrguoDSVGuHUezM6E2joBzq1rqJUkqYxcoUJS6UKAK6+EK67I9o0ZA127wk8/pVeXlK9iTK47VBRqGxpqJUnKlZwH2xjjFzHGsDaX+pFUzoUAl14K11yT7Xv5ZejUKbk4p6S1E2OyVPJ33yXtzarAWesw/bhlpw1Xm9bN+nwv/P6VD37vpAqtzNex3VBCCIUFBQUFXsdWKiduvBHOOSfbbt0a/vc/VyoqR0pmpHL6q71yijFZmK3I/vvDK6+kV48kSeVHzqYtORVZ0tr561/httuy7bfego4dsxf0lLSq5ctXDLWdOhlqJUnaAAy2ktbe6acn17gtGhp8911o3x6mT0+1LKlcWrYsuT50kV69klkOkiQp5wy2ktbNySfD/fdnw+2kSUm4/fbbVMuSypWlS6FaiQsP9OkDjz+eXj2SJFVwBltJ6+6EE+DBB7OjUR99BO3awddfp1qWVC4sWQLVq2fbffvCAw+kV48kSZWAwVbS+vn97+GRR7KjUp99loTbL75ItSwpVYsWJdd/LvLXv8Kdd6ZXjyRJlYTBVtL6++1vYciQ7OjUlClw4IFJyJUqmwULoGbNbPvCC+GGG9KrR5KkSsRgK6lsDj8chg2DTTZJ2l9/nYzcfvRRunVVQgUF2Zs2snnzoHbtbPvKK+Gqq9KrR5KkSsZgK6nsDj0Uhg+HWrWS9rffJgtKTZqUalmVTWFh9qaN6McfoV69bPv66+Gii9KrR5KkSshgKyk3DjkERo2COnWS9vTpSbh9771Uy5I2qNmzYdNNs+1//xvOOSe9eiRJqqQMtpJyp3375DqdRaNX338PBx3kEKIqppkzoXHjbPv+++HUU9OrR5KkSsxgKym39t8fnnsuO4o1Zw507Aivv55uXVIuTZsGTZtm2w8/DCeemF49kiRVcgZbSbnXpg08/zw0apS0585Npiq/8kq6dVVwd9+dvWkD+vpr2GKLbHvoUDj22PTqkSRJhBhj2jWUKoRQWFBQUFDoFEYpf733Hhx8cDIlGZJVY0eOTKYsK+dCyN4vp7/a89/nn8P222fbI0cmi6dJkqT1EX55l7XjiK2kDef//T948UXYbLOkPX9+EgKeey7VsqT18sknK4ba0aMNtZIklRMGW0kb1m67wdix2ambCxZAjx7JCspSvpg0CXbaKdt+6aXk3HFJklQuGGwlbXg77ZSE2623TtqLFkHPnjBsWLp1SWvjnXdg992z7TfegAMOSK8eSZK0CoOtpI1jhx2SUa5tt03aS5bAUUfBY4+lW5e0Jm+8AQUF2fY778A++6RXjyRJKpXBVtLG06JFMnLbsmXSXroUfvc7eOihVMuSSvXSS/Cb32Tb778Pe+2VXj2SJGm1DLaSNq6tt04WlNp556S9fDn06QMDB6ZZlbSi0aOhXbts++OPk/PFJUlSuWSwlbTxbbFFEm6LzluMEU480QuwqnwYOTK57nKRzz+HHXdMrx5JkvSLDLaS0rHZZjBmzIpTO/v2hdtuS68maehQ6N492/7qq+x54ZIkqdwy2EpKT5Mm8Pzz0Lp1tu/MM+GGG9KrSZXXww/DkUdm29OmZVfyliRJ5Vq1tAuQVMk1apScz9i1K7z2WtJ37rnJJYEuuCDd2vJMyYFGraP774c//Snbnjkz+eBFkiTlBYOtpPRtuin873/QrRu8/HLSd+GFsHgxXHophJBufXli+PC0K8hT//43nH56tj17NjRsmF49kiRpnTkVWVL5UK8ePP00dOiQ7bv8crjoomRxKWlDuP76FUPt3LmGWkmS8pDBVlL5UacOjBgBnTtn+665Bs47z3Cr3LvyyuRnq8i8eVC/fnr1SJKk9WawlVS+1KoFw4ateMLoDTfAWWcZbpU7F14Il1ySbc+fn3ywIkmS8pLn2Eoqf2rWhMcfh2OPTS6/AsllgBYvhjvugCp+Jleayy8v/b5WcvbZcPPN2fbChbDJJunVI0mSyizEcjoCEkIoLCgoKCgsLEy7FElpWbIE+vSB//4323fCCXDvvVC1ampllVcl19gqp7/a09e3L9x9d7a9eDFUr55ePZIkVW45WyHUYQ9J5Vf16vDgg0m4LTJwIPzhD7B0aWplKU/17r1iqF2yxFArSVIF4VRkSeVbtWowYEASQO6/P+l7+OEklDz0kMFEa+eII5Jzt4ssXeqovyRJFYgjtpLKv6pV4Z574NRTs32PPQZHHw2LFqVXl/JDp//P3n2HZ1Ue/h9/Hwh7YwBBlO2uI4DUiqywhyK4qoLgHkVtbSu1uEodtO7xc1QRR8EBFAERhTD0CyoaF4KCIIoooggiQ1Zyfn8cyQMCCuRJTvLk/bqu5yL3nfOcfEK4bD8559x35x1LbU6OpVaSpBRjsZVUPJQqBQ88AFdemZgbNw769IkW/5F25Xe/gylToo+DAHJzY1t8rKiuaSFJUiqw2EoqPoIA7roL/vKXxNykSXDSSdF2LdL2jjoKXn89+rhq1ehKbZC0NSry5ObmsmzZMmbOnMnw4cMZMmQI/fr1o0ePHhx//PEccsgh1K5dm7Jly/L73/8+6V8/TkEQMGDAgEL7epMmTaJFixaUL1+e9PR0Bg4cyOrVqwvt60uSii6fsZVUvAQBDBsWbc/yz39Gc1OmQI8eMGECVK4cbz7FLwyhSRNYsiQa16sHy5blq9Tm5uaycOFCFi1axOLFi1m8eDGffvopixcvZsmSJWzaw1viy5Ytu88ZiqLHH3+cpk2bFsrXmjlzJr169aJx48bceuutzJ070WtPmgAAIABJREFUl8cff5wVK1YwadKkQskgSSq6LLaSip8ggKFDoWxZuP76aG7GDOjaNbqCW7VqrPEUozCEOnXg229ZADRu1owyCxbsdaldu3Ytc+bMYdasWcyePZs33niDNWvW7Pb42rVr07hxY5o0aUKTJk1o2LAh6enp1KxZM+9Vo0aNlCu2hXm1dtiwYZQqVYpp06Zx4IEHArB06VJeeukl5s2bxxFHHFFoWSRJRY/FVlLxdd110ZXba66JxrNmRQsFTZ4M1avHm02FLwyhYkXYuJEbgZuAmwcM4NpfKbVhGPL5558ze/bsvCL7wQcfkJubu8Nx9evX5/DDD9+hwDZu3JjGjRtTpUqVAvu2FHnzzTc57LDD8kotQPPmzcnKyuLjjz+22EpSCecztpKKt7/+NXrudps334SOHWHVqvgyqfBtWxRq40b+S1RqS5Uqxe9+97tdHr5hwwbGjh3LOeecwwEHHECjRo04++yz+X//7//x3nvvUapUKVq2bMmVV17Js88+yxdffMEXX3zByy+/zIMPPsif//xnTjnlFI4++uikltoBAwYQBAGjR4/moIMOYv/992fKlClccsklVKpUiVatWvHtt9/mHf/f//6X5s2bU7lyZfbff3/69u3Lp59+utN5tz0Lu3XrVm6//XaOOuqoXf7dzJw5k1atWlG5cmWOPPJIRowYwamnnkr58uWZMWPGbs+7K9s+t2DBArp27UrlypWpX78+//jHP/bp7+bqq6/mz3/+8w5zX331FQA1a9bcp3NKklKHxVZS8XfVVdGKydtkZ0P79rBdASgJLrww8SpRcnPztu+ZDZz306rHd999N+3atcs7bO3atTzzzDOcdtpp1KpVi759+/Lf//6X5cuXU7NmTXr27Mktt9zCjBkzWLNmDXPmzOHuu+/m9NNPp379+oX6LQ0dOpTLLruMFStW0KtXL7788kvOPvts5syZw6hRowB48sknOeecc6hevTr//ve/GTRoUN5zqDk5OTudMzc3l5NOOombbrqJww8/nC5duuzw+Y8++oguXbpQunRphg0bRoMGDRg4cCDVq1fnnnvuoWHDhnv9fSxdupQ2bdpQr149br31VqpWrcoNN9zAuO23X9pD1157Lf37988bL1u2jP/973/Uq1ePVq1a7fX5JEmpxVuRJaWGyy6Lnrm96KLoltQPPoB27SArC/bfP+50heKRR+JOEIOcHEiL/qdsCdC7XDk2b9rEZZddxh/+8Ae+//57xo8fz5gxY3j55Zd3WOSpZcuW9O3bl169enHooYdSKqZtgHbl0UcfpWXLltx7771s2rSJ559/nqVLl/Kf//yHVT/djfDxxx/Tp08fRo4cSbly5QAoU6YM11xzDQsWLODwww/f4ZxjxoyhSZMmLF68mNq1a+/0NUeOHMmmTZt45plnOOiggzjvvPNIT09n9erVXHzxxfv0fUyfPp2hQ4cyZMgQANq0acMxxxxDVlYWvXv33qdzAqxevZpTTjmFjRs3MmrUKCpWrLjP55IkpQaLraTUccEFUbkdODC6ijd/fqLcHnBA3OmUbFu3QpkyAKwBelarxrdr1pCZmcmxxx5L9+7dycrKYsuWLUB0a2zr1q3p27cvffr04aCDDoox/C879thjgWgV5caNG1O+fPmdFp665ZZbAPjiiy94++23eeONN3jyyScB+Oabb3Yqtps3b2bMmDG7LLUAP/74IwDVf3o+vUKFCpQvXz5f++/uv//+/GW77bl+85vfANHV8321bNkyunXrxscff8zjjz9Or1699vlckqTUYbGVlFr694/KTr9+0dW8BQugbVuYNg2KcJHRXtq8OVo4DNgKnHnggcz/4gtq1KjBm2++SVZWFhA9Z9uhQwf69u3LKaecQt26dWMMvefS0tJ2+fH2Zs2axaWXXsrcuXOpVq0axxxzDC1atGDixIm7PP7kk0+mWbNmu/2aPXv25I477uD6669n0KBBjBkzhlWrVtG5c+d9/j6aN2+edzUZyPdV8YULF5KZmcmqVasYO3aspVaSlMdiKyn1/P730ZXbM8+MruotXpwot40axZ1O+bVxI1SokDc8uVEjJv+0Z+3q1auB6IrnmWeeyWmnnUbDhg0J8rGHbVG0bt06TjrpJOrWrcsHH3yQdyV0xIgRuy22lX9lj+eDDjqIunXrcv/993PPPfcAcMEFF3DRRRftc8709PR9fu/Pff3113Tq1Il169aRlZXFb3/726SdW5JU/BWdB4okKZn69oUxY6KCC/DZZ9CmDXzySayxCtJFFyVeKWvDhh1K7cLLLmPST6V2e++++y7XXHMNjRs3Ji0tjf32248Htl9grJhbsGABq1atonfv3nmldtOmTTz11FP7fM7TTjuNHj168PXXX/Pqq6/yxRdf8J///KfIPHt8ySWX5C0YZamVJP2cV2wlpa6TToIXXoDevWHTJli2LHHl9tBD406XdP/5T+LjlFxIat062H5rnVtuodngwZz1/fcsW7aM/fbbj3Xr1vHDDz+wZs2avNeGDRtYtWrVLrfBKa6aNGlCpUqVePTRR0lPT2fDhg089dRTed/jtudl90a1atUYN24c9erV44ADDmDp0qXUrFmT5s2b7/a53MLy9ttv88ILL9CiRQs+++wzRowYscPnmzZtSuvWreMJJ0kqEiy2klJb167w4ovQqxf8+CMsXx6V26wsOPLIuNNpT61ZAz8tagREexdfdRUB0V6uv2TLli1s2LCBatWqFWzGQlS9enUmTpzINddcw7XXXku1atXo06cPN998M3379mXixIl069Ztr845bNgwfve73/H444+zcuXKvHKclpbG3XffzeWXX14Q38oe+fDDD4Go4A4cOHCnz5977rkWW0kq4YL8rHZYkIIgyM7IyMjIzs6OO4qkVDBzJvToAevXR+P99oOpU+GYY+LNlUTbP0ZaRP/Tvm9WrYp+Xts8+CBcckl8eVLQN998wyGHHMIjjzzCaaedBkAYhqxatYoBAwYwa9asvG2GJElKoqQtguEVW0klQ9u28PLL0K0brF0L330HHTrAK69AixZxp9PufPMN1KmTGI8YAeeeG1ucVLVmzRp++OEH7rrrLhYvXkx6ejo5OTksXryY//u//+PQFLx1X5KUWrxiK6lkmTMHunSB77+PxlWrwuTJcPzx8eZKgpS7YvvVVzvuP/zMM3DGGfHlSXETJ07k7rvv5v3332f16tVUqFCBhg0b0qNHD/785z8ndYVjSZJ+krQrthZbSSXPO+9Ap07RLa4AlSvDpElw4onx5sqnlCq2S5dCgwaJ8f/+Fy0CJkmSUknSim3RWMNfkgpTRgZMnw61akXjdeuiRaamTYs3lyKLF+9Yal96yVIrSZJ+kcVWUsl01FEwYwbsv3803rAhWlzq5ZdjjVXiffwxNG2aGGdlRb90kCRJ+gUWW0kl1+GHR6slb3uOc+PGaO/biRPjzVVSzZ0Lhx2WGP/f/0ULfEmSJP0Ki62kku3gg6Nye9BB0XjzZujTJ3qmU4UnOzu6ir7NnDlwwgnx5ZEkScWK2/1IUpMm8Oqr0dXBTz+FLVvgtNNg5Eg4/fS40+2xG26IO8E+ev11+N3vEuP33oOjj44vjyRJKnYstpIE0WJFM2dG5faTTyAnB37/++gK7jnnxJ1uj9x4Y9wJ9sGMGdC+fWI8b150i7gkSdJe8FZkSdqmfv2o3G57zjM3F/r3h+HD482Vql55ZcdSu3ChpVaSJO0Ti60kba9u3egq4m9+E43DEM4/Hx56KNZYKWfCBOjSJTFesgSaNYsvjyRJKtYstpL0c7VrR/vcHntsYu7SS+Hee+PLlEpGj45Wn95m2TJo2DC2OJIkqfiz2ErSruy3X7SH6nHHJeauvBJuvz2+TL+iV6/Eq8h66qloYa5tli9PbLckSZK0j1w8SpJ2p0aN6DnQ7t1h9uxo7i9/gU2b4O9/jzfbLhT57Xf/8x+46KLE+NtvIT09vjySJClleMVWkn5JtWoweTK0aZOYGzIk2lsnDOPLVdzcd9+OpXb1akutJElKGoutJP2aKlVg0iTIzEzM/eMfcO21lts98a9/wRVXJMY//ADVq8eXR5IkpRyLrSTtiUqVopV8u3ZNzN12G1x9teX2l9x0E1xzTWK8bl30iwJJkqQksthK0p6qUAHGjdtxdaa77oJBg6I9b7Wja66BG29MjDdsiH5BIEmSlGQWW0naG+XKRdvV9O2bmHvgAbj4Ysvt9gYNim5B3mbjxugXA5IkSQXAYitJe6tsWXjmGTjzzMTco4/CeedBTk58uYqK88+H++9PjDdvjn4hIEmSVEAstpK0L9LS4OmnoX//xNwTT0C/frB1a3y54vb738Pw4Ynxli1Qpkx8eSRJUolgsZWkfVW6NDz+OFxwQWJu1Kio3G3ZEl+uuJx0UnQle5utW6NfAEiSJBUwi60k5UepUvDww3DZZYm50aPh1FNh06b4chW2Dh2iVaO3ycmJir8kSVIh8FfpkpRfpUpFz5SWLQt33x3NjR8Pp5wCY8YU2qJJDz9cKF9mZ8cdB2+9FX2clhY9UxsEMYWRJEklkcVWkpIhCODOO6NFkoYNi+Zeeim6PfeFF6BixQKPcNFFBf4ldnbEETB/fvRxjRrw3XeWWkmSVOi8FVmSkiUI4NZb4frrE3NTp0KPHrBuXXy5CkIYwkEHJUpt/fqWWkmSFBuLrSQlUxDATTfB0KGJuRkzoGtX+OGH2GIlVRjCfvvBF19E48MOiz621EqSpJhYbCWpIAwZAv/6V2I8axZ06gSrV8eXKRnCMHqWeNv30bJl4qqtJElSTCy2klRQ/vKXxGJSAHPmQGZmdMtuAWjePPEqELm50UJZ2/bpbd8++p4kSZJi5uJRklSQrrwyWlDq0kuj8bvvRoVw6lSoXTupX+qdd5J6uh3l5Oy4J23Pnjtu7yNJkhQjr9hKUkG75BJ47LHEM6hz50bldvnyeHPtqa1bdyy1Z5xhqZUkSUWKxVaSCsN558GTT0a38kL0XGq7dvDll7HG+lVbtkCZMonxwIHwzDPx5ZEkSdoFi60kFZZzzoGRI6F06Wi8cCG0aQOffx5vrt3ZvDlaKGqbP/wBhg+PL48kSdJuWGwlqTCdcQY891ziKuinn0LbttGfRcnGjdGzwdv89a9w333x5ZEkSfoFFltJKmx9+sDYsYmroZ9/HpXbTz6JN9c269dDhQqJ8Q03wLBh8eWRJEn6FRZbSYpDz54wfjyULx+Nly2Lbkv+6KN4c61dC5UrJ8a33QY33hhbHEmSpD1hsZWkuHTpAhMnJq6Ofv11dOV27tx48nz/PVStmhjfcw9cc008WSRJkvaCxVaS4pSZCZMnJ66SfvtttBXQu+8Wbo6VK6FGjcT4kUfgiisKN4MkSdI+sthKUtzatIFXXklcLf3uO+jQAd56q3C+/ooVUKtWYvzkk3DhhYXztSVJkpIgLe4AkiTg+ONh6lTo3Dm6Jfj776FjR3jpJfjd7/boFOPH78PX/fJLqF8/MX72WTj99H04kSRJUnyCMAzjzrBLQRBkZ2RkZGRnZ8cdRZIKz7vvQqdO0VVbgEqVYNKk6Kpusn32GTRqlBi/8AKcdFLyv44kSdKuBck6kbciS1JRcuyxMH061K4djdevh65dISsruV9n0aIdS+3LL1tqJUlSsWWxlaSi5je/gRkzoG7daPzjj9H2QJMnJ+f88+dDs2aJ8fTp0S3QkiRJxZTFVpKKosMOg5kzE8+/btwIJ58MEybk77zvvw9HHJEYz5oF7drl75ySJEkxs9hKUlHVrBm8+io0aBCNN2+GPn1g7NhdHl6vXuK1S2+9Bcccs+N4DxemkiRJKsostpJUlDVqFF25bdw4Gm/dGq1a/MwzOx26fHnitZNZs+C44xLj99+HFi0KJrMkSVIhc7sfJdXCFWuZtWgl6zZupXL5NE5oms7BdarEHUsq3ho0iK7cdugACxdCTg6cfXZ0Bbd//19//7RpkJmZGH/0ERx6aMHllVLZf0+DT17Zu/c06wxnP18weSRJQJKKbRAEBwN3A22BH4BXgavDMFyWjPOr6Ju1aCX3ZH3CnCWrdvrccY1qcmVmM05omh5DMilFHHBAdOU2MzNa/Ck3FwYMgC1b4Pzzd/++yZOhW7fEeNEiaNKkwONKKWtvS+2+vkeStFfyfStyEASVganAEcANwENAV2B6EAQV8nt+FX3PvrWUfo+9uctSCzBnySr6PfYmz731RSEnk1LM/vtHqyUfdVQ0DkO44AJ48MFdH//CCzuW2s8+s9RKkqSUlIwrtpcDBwK/CcPwQ4AgCL4nuoJ7EvBsEr6GiqhZi1byt7FzyQ1/+bjcEAaP/YADalTwyq2UH7VqRbcWd+4M77wTzV12WXRbMlcmjnv2WTjzzMR42bLoqq8kSVIKSsbiUS2Bz7aV2p8s/OnPhkk4v4qwe7I++dVSu01uCPdmfVKwgaSSYL/9ICtrx8Wgrrpqx2O2L7UrVlhqJUlSSst3sQ3D8NQwDBv9bHrbJonee5rCFq5Yu9vbj3fnzSWrWLhibQElkkqQ6tVhyhQ44YRfPm7lSqhdu3AySZIkxSSpqyIHQVAXaAf8DVgMjN+D92Tv5lMu2VnEzVq0cp/f50rJUhJUrRotDtWrV/Ts7c+tXh0VYEmSpBSX7H1s/wOMBDYCvcMwXJfk86sIWbdxa6G+T9IuVK4ML74IHTvuOD9oEFSrFk8mSZKkQpbsfWxvBqYBVwGvBkHQPgzD93/pDWEYNt/V/E9XcjOSnE9JVLn8vv3z2df3SdqNihVhwgTYfh36++6DUqXgrrsgCGKLJkmSVBiSesU2DMPXwzC8E+gL1AD+kczzq2jZ19WNXRVZKgDly/P27M283fZq3uan3xfecw9cfnm0560kSVIKy1exDYIgPQiCj4MguPdnn5r305+H5Of8KtoOrlOF4xrV3Kv3tGpU0+drpQLS/PiyNJ9yG81PbZyYfPBBuOgiy60kSUpp+b1iuwpIB3oHQVBuu/lte1Aszuf5VcRdmdmMUnt4l2OpAK7IbFawgaSSrkwZGDUKzjorMffYYzBwIOTkxJdLkiSpAOWr2IZhmAvcChwITA+C4IogCP4OjAI2Ez1zqxR2QtN0bu3zm18tt6UCuK3PUd6GLBWGtDR48kk499zE3JNPwjnnwJYt8eWSJEkqIMnYx/YO4FQgAIYCg4A5wHFhGM7O7/lV9J3R8iCeOr8VrXZzW3KrRjV56vxWnN7ywEJOJpVgpUvD8OFw4YWJuWeegTPPhM2b48slFXfNOhfOeyRJeyUIwzDuDLsUBEF2RkZGRnb27ra5VVG0cMVaZi1aybqNW6lcPo0Tmqb7TK1USLZf/DjvP+25uXDFFfDAA4lP9uoFzz8P5cohSZIUo6Rt3eC+K0qqg+tUschKRUmpUtHWP+XKwZ13RnMTJkDv3jB2LFSo8MvvlyRJKgaSut2PJKkICgK4/Xb4298Sc5MnR1du16+PL5ckSVKSWGwlqSQIArj5ZrjhhsRcVhZ07w5r18aXS5IkKQkstpJUUgQB3HhjVHC3efVV6NIF1qyJLZYkSVJ+WWwlqaS59tro1uRtXn8dOnWC1avjyyRJkpQPFltJKomuvhruvTcxfustyMyElSvjyyRJkrSPLLaSVFINGgQPPZQYv/sudOgA33wTXyZJkqR9YLGVpJLs4oth+PDEJrhz50K7drB8eayxJEmS9obFVpJKuoED4cknoz1vAT76CNq2hWXL4s0lSZK0hyy2kiQ45xwYNQpKl47Gn3wCbdrAZ5/FGkuSJGlPWGwlKUV8+WXitU9OPx1Gj4YyZaLxkiXRldvFi5OWUZIkqSBYbCUpRdSrl3jts9694X//g7Jlo/HSpVG5XbAgKRklSZIKgsVWkrSjHj1gwgQoXz4af/llVG7nz483lyRJ0m5YbCVJO+vcGV58ESpWjMYrVkSrJX/wQayxJEmSdsViK0kp4quvEq+k6NABJk+GypWj8bffQvv28M47SfoCkiRJyWGxlaQUccABiVfSnHgiTJkCVatG41WrIDMT5sxJ4heRJEnKH4utJOmX/fa3kJUFNWpE4++/h44dYdaseHNJkiT9xGIrSfp1LVrAtGmQnh6N166FLl1gxoxYY0mSJIHFVpK0p445BqZPhzp1ovH69dC9O0ydGm8uSZJU4llsJUl77sgjo6u0detG4x9/hJ494aWXYo0lSZJKNoutJGnvHHoovPoqHHhgNN60CXr3hvHj480lSZJKLIutJGnvNW0KM2dCw4bRePNm6NsXRo+ONZYkSSqZLLaSpH3TqFFUbps0icZbt8KZZ8LIkfHmkiRJJY7FVpK07w46KLot+ZBDonFODvTrB088EW8uSZJUolhsJUn5U69edOX2iCOicW4uDBwIjz4aby5JklRiWGwlSflXp060FdDRR0fjMIQLL4QHHog3lyRJKhEstpKUIsIw8YpFrVowbRo0b56Y+8Mf4K67YgokSZJKCoutJCl5ataEqVPht79NzP3pTzBsWHyZJElSyrPYSpKSq3p1eOUVaN06MTd4MAwdGl8mSZKU0iy2kqTkq1IFJk+G9u0Tc9dfD0OGxHivtCRJSlUWW0lKEdnZiVeRUKkSTJwInTol5m6+Gf76V8utJElKqrS4A0iSkqNFi8THRaY3VqwI48fDqafCiy9Gc7ffDps3w913QxDEm0+SJKUEr9hKkgpW+fIwdiz07p2Yu/deuOyyaM9bSZKkfLLYSpIKXtmy8NxzcPrpibmHHor2us3JiS+XJElKCRZbSVLhKFMG/vtfOPvsxNzw4TBgAGzdGlssSZJU/FlsJUmFJy0NnngiKrPbPP10VHa3bIktliRJKt4stpKkwlW6NDz2GFx8cWLuuefgjDOiRaUkSZL2ksVWklT4SpWCBx+EQYMSc//7H/TtCxs3xpdLkiQVSxZbSVI8ggDuuQeuvjoxN3EinHwy/PhjfLkkSVKxY7GVJMUnCODf/4Zrr03MvfIK9OgB69fHl0uSJBUrFltJUryCAP75T7jppsTc9OnQrRusXRtfLkmSVGxYbCUpRdStm3gVO0EA118Pt96amHvtNejcGdasiS+XJEkqFiy2kpQivvoq8Sq2Bg+GO+9MjN94Azp2hFWr4sskSZKKPIutJKlo+eMf4f77E+O334bMTFi5Mr5MkiSpSLPYSpKKnssvh4cfjm5RBnjvPWjXDlasiDWWJEkqmiy2kqSi6aKLYPjwRLmdNy8qt8X6XmtJklQQ0uIOIElKjgkTEh/36hVfjqQaMADKloX+/SEnBz7+GNq2hWnT4MAD404nSZKKCIutJKWIk05KfByG8eVIurPOgjJloj+3boVFixLltmHDuNNJkqQiwFuRJUlF32mnwejRUcEFWLIE2rSJSq4kSSrxLLaSpOLh5JNh3DgoVy4af/FFdOX244/jzSVJkmJnsZUkFR/du0cPE1eoEI2/+ipaUGrevFhjSZKkeFlsJUnFS6dOMGkSVKoUjVesiMrt++/HGkuSJMXHYitJKn7atYOXX4YqVaLxypXQvj1kZ8caS5IkxcNiK0kqnk44AaZMgWrVovHq1ZCZCW+8EW8uSZJU6Cy2kqTiq1UryMqCmjWj8Zo10Lkz/N//xZtLkiQVKoutJKl4a9482tM2PT0ar10LXbvCjBmxxpIkSYXHYitJKv6OPjoqsnXqROP166MVlKdMiTWWJEkqHBZbSUoRGRmJV4l0xBEwcybUqxeNf/wRevWKVlCWJEkpzWIrSSkiOzvxKrEOOSQqtwceGI03bYLevWHcuHhzSZKkAmWxlSSllqZN4dVXoVGjaLxlC5x2Gjz/fLy5JElSgbHYSpJST8OG0ZXbZs2i8datcOaZMHJkrLEkSVLBsNhKklLTgQdGC0odemg0zs2Fc86BESPiTCVJkgpAWtwBJEnJ8cgjiY8vuii+HEVKvXpRue3YET78EMIQBg6EzZv9S5IkKYUEYRjGnWGXgiDIzsjIyMgu0augSNKeC4LEx0X0P+3xWbkSOnWC995LzN13H/zhD/FlkiRJwa8fsme8FVmSlPrS0yErC1q0SMwNGgR33hlfJkmSlDQWW0lSyVCzJkydCscfn5i7+mq49db4MkmSpKSw2EqSSo5q1eDll+HEExNz114LN93k/duSJBVjFltJUslSpQq89BJ06JCYu/FG+PvfLbeSJBVTFltJUslTqRJMnAhduiTmbr0V/vIXy60kScWQxVaSVDJVqADjxkHPnom5O+6AK6+03EqSVMxYbCVJJVf58jBmDJxySmLuvvvgkksgNze+XJIkaa9YbCVJJVvZsvDss3DGGYm5Rx6B88+HnJz4ckmSpD1msZUkqUwZePpp6NcvMTdiBPTvD1u3xhZLkiTtmbS4A0iSkmP7R0W1D9LS4PHHo5I7fHg0N3IkbNkC//1vNC9Jkooki60kpYgJE+JOkAJKl4b//AfKlYMHH4zmnn8+KrfPPBPNS5KkIsdbkSVJ2l6pUvDAA9HqyNuMGwd9+sDGjfHlkiRJu2WxlSTp54IA7ror2td2m0mT4KSTYMOG+HJJkqRdsthKkrQrQQDDhsGQIYm5KVOgRw9Yty6+XJIkaSc+YytJKeLGG3f9sfIhCGDo0GhLoOuvj+ZmzIBu3eDFF6Fq1VjjSZKkSBCGYdwZdikIguyMjIyM7OzsuKNIUrEQBImPi+h/2ou3YcNg8ODEuFUrmDwZqlePL5MkScVb8OuH7BlvRZYkaU9ccw3ceWdi/Oab0LEjrFoVXyZJkgRYbCVJ2nN//GO0YvI22dnQvj18+218mSRJksVWkqS9ctll0V632+79/uADaNcOvv461liSJJVkFltJkvbWBRfAiBHRnrcA8+dH5fbLL+NMJUlSiWWxlSRpX/TvD08/DaVLR+MFC6BtW1i6NN5ckiSVQBZbSZJ5HfWcAAAgAElEQVT21e9/D88+C2k/7Z63eHFUbpcsiTeXJEkljMVWkqT86NsXxoyJ9roF+OwzaNMGFi2KNZYkSSWJxVaSpPw66SR44QUoVy4aL1sWlduPP443lyRJJYTFVpKkZOjaFSZOhAoVovHy5dFtyR9+GG8uSZJKgLS4A0iSkuPCC+NOIDp2hJdegh49YP16+OabaLXkqVPhmGPiTidJUsoKwjCMO8MuBUGQnZGRkZGdnR13FEmS9s6sWdCtG6xdG41r1IBXXoEWLeLNJUlS0RIk60TeiixJUrKdcEJ0lbZ69Wi8ejVkZsLrr8ebS5KkFGWxlSSpIBx3HGRlQc2a0fiHH6BzZ3jttXhzSZKUgiy2kiQVlIwMmD4datWKxuvWRYtMTZsWby5JklKMi0cpqRauWMusRStZt3ErlcuncULTdA6uUyXuWNpD/vyKt4suSnz8yCPx5dDPHHUUzJgR3Yr89dewYUO0uNS4cdClS9zpJElKCUlZPCoIgkOBO4A2RFeBZwFXh2E4Nx/ndPGoYmTWopXck/UJc5as2ulzxzWqyZWZzTihaXoMybQn/PmlhmC75ReK6LqAJdvChdChA3z5ZTQuWxbGjIGePePNJUlSfJK2eFS+i20QBPsB84BKwL+B0sBfgTXAwWEY/rCP57XYFhPPvrWUv42dS+4v/FMqFcBtfY7i9JYHFl4w7RF/fqnDYlsMLF4cldulS6NxmTLw7LNwyinx5pIkKR5FalXk84E6wIVhGP4jDMMbgJt/muuXhPOrCJu1aOWvliKA3BAGj/2AWYtWFk4w7RF/flIha9IEXn0VGjWKxlu2wGmnwXPPxZtLkqRiLhnFttVPf76y3dy2y6yHJuH8KsLuyfrkV0vRNrkh3Jv1ScEG0l7x5yfFoEGDqNw2axaNc3Lg97+Hp5+ON5ckScVYMortWOA6YPV2c/V++nPnB/aUMhauWLvLZzJ/yZtLVrFwxdoCSqS94c9PilH9+jBzJhx2WDTOzYX+/WH48HhzSZJUTOV7VeQwDP+7/TgIgjTgUiAEXvi19wdBsLuHaL3aW8Tt622psxatdKXdIsCfnxSzunWj1ZI7doS5c6MHo88/P7o9+eKL404nSVKxktR9bIMgKAU8CDQH/h2G4TvJPL+KlnUbtxbq+5Rc/vykIqB27Wif22OPTcxdcgncd198mSRJKoaSto9tEATlgCeAM4CHgcF78r4wDJvv5nzZQEay8in5Kpfft38++/o+JZc/P6mI2G8/yMqK9rR9661o7oorYNMm+POf480mSVIxkZQrtkEQVAQmE5Xaf4ZheEmYjA1yVaTt676m7odaNPjzk4qQGjVgyhT43e8Sc3/5C9x8c3yZJEkqRvJdbH+6/XgM0Ba4LAzD6/KdSsXCwXWqcFyjmnv1nlaNavp8ZhFxcJ0qtGxQjY2fzyU3N3eP3uPPTypA1arB5MnQpk1ibsgQuOEGNyaWJOlXJOOewiuArsD1YRg+mITzqRi5MrMZ/R57c4+2jCkVwBWZzQo+lH7VwoULGT58OBPvvY+NP26g0pEdSO/xp198jz+/ou+GG+JOoHyrUgUmTYKTT45uTwb4xz9g82a45RYIkraPvSRJKSVfxTYIggrA34Hvga+CIBjws0PWhWE4Oj9fQ0XbCU3TubXPb/jb2Lm/WG5LBXBbn6O8jTVGP/zwA8899xyPP/44s2fP3uFzZWrU/cX3+vMrHm68Me4ESopKlWDCBOjTJ7qCC3DbbdEzt3fcYbmVJGkXgvw8ChsEQUNgyS8c8nkYhg338dzZGRkZGdnZu9sNSEXJrEUruTfrE97cxb6orRrV5IrMZpaiGOTm5jJz5kwef/xxxowZw4YNGwAoU6YMW7ZsASAIAsa/Po+n3vven59UlGzaBKedFpXcbS6/HO69F0oldVMDSZLikrTf1uar2BYki23xtHDFWmYtWsm6jVupXD6NE5qm+0xmjC6++GIeeeSRvHGbNm2oUaMGL7zwAqVKlSI3N5fjjjuON998E/DnJxU5mzfDWWfBmDGJuQsugIcfttxKklJB0oqt+3YoqQ6uU8UiVITUqlWLww47jL59+zJgwABGjx7N4MGDKVWqFJmZmUyZMoXu3bvnHe/PTypiypaFZ56Bfv2iPwEefRS2bIHHHoPSpePNJ0lSEeEVW6kECMOQv/3tbwwbNowgCBgxYgTXX389n3/+OXPmzKFly5ZxR1QS9OqV+Hj7u1eVAnJy4Lzz4MknE3NnnQVPPAFp/o5aklRsecVW0p7ZsmUL559/Pk899RRpaWkMHz6c5s2b8/nnn1OrVi2aN28ed0QlycSJcSdQgSldGh5/PLqC++ij0dzIkdGtyiNHQpky8eaTJClmPqAjpbB169bRq1cvnnrqKSpVqsSECRPo168fL774IgDdunWjlM/pScVDqVLRs7WXXpqYGz0aTj01WmhKkqQSzP9HK6WoFStW0K5dO15++WVq1arFjBkz6Nq1KwCTJk0CoEePHnFGlLS3SpWCBx6Aq65KzI0fD6ecAj/+GF8uSZJiZrGVUtDixYs54YQTyM7OpnHjxsyePZsWLVrkfX7ZsmVUqlSJTp06xZhS0j4JArjzTrjmmsTcSy/BSSfBT1t6SZJU0lhspRTz9ttvc/zxx7N48WIyMjKYPXs2TZs23eGYSZMm8cYbb1CjRo2YUkrKlyCAW2+F669PzE2dCj16wLp18eWSJCkmFlsphbz88su0a9eOb7/9ls6dOzNjxgzq1Kmz03HNmjXjyCOPjCGhpKQJArjpJhg6NDE3YwZ07Qo//BBbLEmS4mCxlVLEU089Rc+ePVm/fj3nnHMOEyZMoEoV96SVUt6QIfCvfyXGs2ZBp06wenV8mSRJKmQWW6mYy83NZejQofTv35+tW7fy17/+lSeeeIKyZcvGHU1SYfnLX+DuuxPjOXOgY0f47rv4MkmSVIgstlIx9vXXX9OlSxeu/+k5u7vuuothw4a5hY9UEl15JTz4YGL8zjvQoQN88018mSRJKiT+v1+pmJoyZQpHH300U6dOJT09nUmTJnHV9luASCp5LrkEHnssev4W4IMPoH17WL483lySJBWwtLgDSNo7W7Zs4YYbbuC2224jDEPat2/P008/Tb169eKOppg9/HDcCVQknHcelCkDAwZAbi7Mnw/t2sG0aXDAAXGnkySpQFhspWLk888/56yzzmL27NmUKlWKm266iWuvvZbSpUvHHU1FwEUXxZ1ARUa/flC2LJx9NuTkwMKF0KZNVG4bNIg7nSRJSeetyFIxMW7cOI455hhmz57NAQccwPTp07nuuusstZJ27Ywz4Lnnoqu3AJ9+Cm3bRn9KkpRiLLZSEbdx40YGDRrEKaecwvfff0/Pnj157733aNOmTdzRJBV1ffrA2LHR1VuAzz+Pyu0nn8SbS5KkJLPYSkXYwoULOf7447n//vspU6YMd955J+PHjyc9PT3uaJKKi549Yfx4KF8+Gi9bFt2W/NFH8eaSJCmJLLZSEbRlyxbuvPNOMjIyeO+992jcuDGzZ8/mj3/8I8G21U6ln2nePPGSdtClC0ycCBUqROOvv46u3M6dG28uSZKSxGIrFTEzZszg2GOP5eqrr2b9+vWceeaZvPPOO7Ro0SLuaCri3nkn8ZJ2kpkJkydD5crR+Ntvo62A3n033lySJCWBxVYqIr766ivOPvts2rdvz7x582jSpAkvvvgio0aNolq1anHHk5QK2rSBV16BqlWj8XffQYcO8NZb8eaSJCmfLLZSzLbddnzIIYcwcuRIypcvz9ChQ/nwww/p3r173PEkpZrjj4epU6F69Wj8/ffQsSPMnh1vLkmS8sFiK8Vo5syZebcdr1u3jpNPPpmPPvqIIUOGUH7bQi+SlGwtW0Z72u63XzT+4Qfo3BlefTXeXJIk7SOLrRSDbbcdt2vXbofbjseNG0fDhg3jjiepJDj2WJg+HWrXjsbr10PXrpCVFW8uSZL2gcVWKkTbbjs+9NBD8247/sc//uFtx5Li8ZvfwIwZULduNP7xx2h7oMmTY40lSdLesthKhWDr1q088cQTHHrooVx99dWsXbuWk08+mfnz53Pdddd527Gk+Bx2GMycCfXrR+ONG+Hkk2HChHhzSZK0Fyy2UgHKyclh5MiRHHHEEQwYMIBPP/2Ugw8+mIkTJzJu3DgaNWoUd0RJgmbNoudrGzSIxps3Q58+MHZsvLkkSdpDFlupAIRhyPPPP89RRx3F2WefzcKFC2ncuDFPPPEE8+bNo0ePHnFHlKQdNWoUXblt3Dgab90Kp58OzzwTby5JkvaAxVYqAA899BCnn3468+fPp0GDBjz66KN8/PHH9O/fn7S0tLjjSdKuNWgQXbk9+OBonJMDZ58NTz4Zby5Jkn6F/w9bKgBHHHEEbdu25cwzz+S8886jbNmycUdSCTB+fNwJlBIOOCC6cpuZCfPnQ24uDBgAW7bA+efHnU6SpF0KwjCMO8MuBUGQnZGRkZGdnR13FEmSSp5vv4WOHeGDDxJz/+//waWXxpdJkpRqgmSdyFuRJUnSzmrVgmnTICMjMXfZZXDPPfFlkiRpNyy2kiRp1/bbD7Ky4LjjEnNXXQX/+ld8mSRJ2gWLrSRJ2r3q1WHKFDjhhMTcNdfAP/8ZXyZJkn7GYitJKaJevcRLSqqqVWHyZGjXLjF33XVw/fVQRNfqkCSVLBZbSUoRy5cnXlLSVa4ML74YLSi1zdChMHiw5VaSFDuLrQrdiBEjuPvuu+OOkW+p8n1I0h6rWBEmTIBu3RJz//oX/PGPlltJUqzc7keFrl27dnz22Wd89tlncUfJl1T5PpQ6gu0WzC+i/2lXqti0Cc44A154ITF36aVw//1Qyt+ZS5L2mNv9SJKkmJQrB88/D6eemph78EG46CLIzY0vlySpxLLYxuzNN9+kS5cu1KpViypVqtCqVSsmTpy4wzHt2rWjYcOGO733ggsu2GE+CAL69+/P3/72N2rXrk2FChXo3LkzH3744Q7v29PjAN5//3169uxJlSpVqFq1Kr1792b+/Pk7HdewYUPa/bSoyGOPPcZxxx3HgQcemPf5ESNGEAQBQRAwc+ZMPv/887xxEOz9L2pWr15N+fLlueSSS3b6XNu2bWnUqBHb340wb948+vTpQ82aNalWrRpt27bltdde2+m9b731Fl27dqVKlSqkp6fTqVMnZs+evc/fR7L+/iSpyClTBkaNgrPOSsw99hgMHAg5OfHlkiSVSGlxByjJli5dSseOHdl///0ZPHgwFStWZMyYMfTu3Zs33niDFi1a7PU5n3/+eWrWrMnVV1/NunXruOeee2jdujXvvffeDiV4T46bPXs2HTt2pHr16gwePJjc3Fzuu+8+WrVqxcyZM8nIyNjp61988cWMGDGC7t2706pVq7z51q1b8/jjjwNw2223sXLlSm6//fa9/v62qVGjBj179mT06NHcd999lClTBoBly5bx2muv8fe//z2vaL777ruceOKJNG3alCFDhpCWlsaTTz5Ju3btyMrKyiuU06ZNo1u3btSuXZtrr72WsmXL8sgjj9C2bVumTp1K27Zt9+r7SObfnyQVSWlp8OSTUcl94olo7sknYfPmxLwkSYUhDMMi+QKyMzIywlQ2evToEAjHjRuXN7dmzZrw4osvDqdMmZI317Zt27BBgwY7vf/888/fYR4I09LSwgULFuTNTZw4MQTCQYMG7fVxhx9+eFi1atVw2bJleXOLFy8OK1asGLZo0WKHLA0aNAgrVqwY1q9fP1y4cOEvft+7+3721rhx40IgnDhxYt7cv//97xAIP/7447y5du3ahXXr1g2XLFkSLl++PFy+fHm4cOHCsFy5cmGvXr3yjmvWrFlYvXr1cPny5XlzixcvDoHwjDPO2Ovvo6D+/qTdiZ6sjV5SocrJCcMLL9zxH2GfPmG4aVPcySRJRVvS+qNXbGPUsmVLypcvz2233UYYhjRv3pwDDzyQhx56aJ/P2bp1aw4++OC8cY8ePahcuTKvv/76Xh23aNEi5s+fz3nnnccBBxyQd1zjxo05+eSTGTVqFF9++eUOn9uwYQMvvvgizZo12+f8e6N79+6kp6czatQoevToAcCoUaNo1aoVhxxySF6m1157jZycHBo1arTTOd577z0APvnkEz755BMuuOAC9t9//7zPN27ceIdbmvdUcfj7k6SkKVUKHnoIypaFBx6I5saOjZ7Bff756JlcSZIKkMU2RgcddBATJ07k1ltvpV+/fqxbt4569eoxYMAAbrzxxrzba3fnhx9+2Glu+1K2TXp6Ot9///1eHbdixQoA6tevv9Nx2579XLFixQ7FrHnz5nm39RaGMmXKcMYZZ/DEE0+wYcMGli1bxjvvvMP999+fd8zq1avJycmhS5cuXHXVVTudo2zZsgB88803ADt8P/lRHP7+JCmpSpWC++6LSuydd0ZzEyZA795Rya1QId58kqSUZrGNWWZmJpmZmYRhyOLFixkxYgQ333wzFSpUYMiQIb/43g8++GCnuWXLlu0wzs3N5ZtvvuHII4/cq+Pq1Kmzy+MAvvjiix2O2aZy5cq/mLcg9OvXjwceeIAJEybw0Ucf5ZXdbWrUqEHp0qUpXbo0Xbt23eG9CxcuZO3atQDUrl0bgC+//HKnr3HdddfxzTff8PDDD+9xruLy9ydJSRUEcPvt0ZXb226L5iZPhl69oq2BKlWKN58kKWW5KnKMrrvuOurXr8+KFSsIgoCmTZsydOhQqlWrxpw5c/KOq1y5Mt9++y1btmzJmxs9ejQLFizY6ZyzZ89m3rx5eeMXXniBDRs20Lp16706rmnTphx++OGMGTOGr776Ku+4zz77jBdeeIHmzZvv89XN9PR01q5du0+3+P5cq1atOPjgg3n66acZNWpU3u3J21SsWJETTzyR6dOns3Tp0rz5devW0bp1ay688EIAmjVrRrNmzRg7dmze1VuA7777jnvuuWeXv0T4pe+jIP/+pN15++3ES4pNEMAtt8ANNyTmsrKge3f46ZeJkiQlm1dsY5SZmcnNN99M165dOfvss6lSpQqTJ09mzZo1dOjQIe+49u3b8+KLLzJ48GD+9Kc/MWvWLC6//HIOO+wwNmzYsMM5K1euTKdOnbjyyitZt24dd999NzVq1NjpNtw9Oe6hhx6ic+fOtGzZkssvvzxvVd8gCPL1HHCvXr0YM2YMAwcOpHXr1ixfvpz169dz27bf7u+lfv36cd111wFwyy237PT5O++8kxNPPJEWLVpw2WWXsd9++/HUU0/x3XffMXz48LzjHnzwQbp3706LFi249NJLKVu2LI899hg//vgj//znP/f6+yiovz9pd5o3jzuB9JMggBtvjK7c/v3v0dyrr0KXLvDSS1CtWqzxJEkpKJkrUSXzRQlYFTkMw/CVV14JO3XqFNauXTssX758eNhhh4V33HHHDsds2rQpHDRoUFi7du2wYsWKYevWrcOZM2fuclXkc889Nxw6dGhYp06dsHz58mGnTp3CDz/8cIfz7elxYRiG77zzTtitW7ewUqVKYeXKlcNevXqFc+fO3em4Bg0ahG3btt3j7/v2228PmzRpEqalpYX77bdfeOONN+7xe39uyZIlYRAEYfXq1cONGzfu8pi5c+eGvXv3DqtVqxZWqVIlPPHEE8OpU6fudNycOXPCzp07h5UqVQrT09PDrl27hm+//fY+fx8F9fcnScXG7bfvuFpyy5ZhuGpV3KkkSUVD0vpjECbhdtCCEARBdkZGRkZ2dnbcUYqNIAg499xzGTFiRFKOkyQpKe67D664IjE+9lh45RXY7tERSVKJFCTrRD5jK0mSCtagQdF2QNu8+y506ADbrWkgSVJ+WGwlKUUEQeIlFTkXXwzDhyf+gc6dC+3awfLlscaSJKUGF49KIXt6W3lRvf1ckpTiBg6EMmXg3HMhNxc++gjatoVp02AX+35LkrSnvGIrSZIKzznnwKhRULp0NP7kk6jcfv55vLkkScWaxVaSJBWu00+H55+Prt4CfPoptGkDixfHm0uSVGxZbCVJUuE75RQYOzba6xZg6dLoyu2CBfHmkiQVSxZbSZIUj549YcIEKF8+Gn/5ZVRu58+PN5ckqdix2EqSpPh07gwvvggVK0bjFSui1ZI/+CDWWJKk4sViK0mS4tWhA0yeDJUrR+Nvv4X27eGdd+LNJUkqNiy2kiQpfieeCFOmQNWq0XjVKsjMhDlz4s0lSSoWLLaSJKlo+O1vISsLatSIxt9/Dx07wqxZ8eaSJBV5FltJklR0tGgB06ZBeno0XrsWunSBmTPjzSVJKtIstpKUIr78MvGSirVjjoHp06FOnWi8fj106wZTp8abS5JUZFlsJSlF1KuXeEnF3pFHwowZULduNP7xx2h7oJdeijWWJKlosthKkqSi6dBDo1uQ69ePxps2Qe/eMH58vLkkSUWOxVaSJBVdzZrBq69Cw4bRePNm6NsXRo+ONZYkqWix2EpSivjqq8RLSimNGkVXbps0icZbt8KZZ8KoUfHmkiQVGRZbSUoRBxyQeEkp56CDoiu3hxwSjXNy4Jxz4Ikn4s0lSSoSLLaSJKl4qFcvunJ7xBHRODcXBg6ERx+NN5ckKXYWW0mSVHzUqRNtBXT00dE4DOHCC+GBB+LNJUmKlcVWkiQVL7VqwbRp0Lx5Yu4Pf4C77oovkyQpVhZbSZJU/NSsCVOnwm9/m5j7059g2LD4MkmSYmOxlSRJxVP16vDKK9C6dWJu8GAYOjS+TJKkWFhsJUlS8VWlCkyeDO3bJ+auvx6GDImev5UklQgWW0mSVLxVqgQTJ0KnTom5m2+Gv/7VcitJJYTFVpIkFX8VK8L48dCjR2Lu9tvhqqsst5JUAlhsJUlSaihfHsaOhd69E3P33guXXRbteStJSlkWW0mSlDrKloXnnoPTTkvMPfRQtNdtTk58uSRJBcpiK0kpIgwTL6lEK1MGRo6Es89OzA0fDgMGwNatscWSJBUci60kSUo9aWnwxBNRmd3m6aejsrtlS2yxJEkFw2IrSZJSU+nS8NhjcPHFibnnnoMzzoDNm+PLJUlKOoutJElKXaVKwYMPwqBBibn//Q/69oWNG+PLJUlKKoutJKWI7OzES9J2ggDuuQeuvjoxN3EinHwy/PhjfLkkSUmTFncASVJytGiR+NgFpKSfCQL497+hXDm45ZZo7pVXon1vJ0yASpXizSdJyhev2EqSpJIhCOCf/4SbbkrMTZ8O3brB2rXx5ZIk5ZvFVpIklRxBANdfD7femph77TXo0gXWrIkvlyQpXyy2kiSp5Bk8GO64IzF+/XXo2BFWrYovkyRpn1lsJUlSyfSnP8F99yXGb78NmZmwcmV8mSRJ+8RiK0mSSq4//AEefji6RRngvfegXTtYsSLWWJKkvWOxlSRJJdtFF8Hw4YlyO29eVG6/+irWWJKkPWexlSRJGjAAnn4aSpeOxh9/DG3bwhdfxBpLkrRnLLaSJEkAZ50Fo0ZBWlo0XrQoKreffRZrLEnSr7PYSpIkbXPaaTB6NJQpE42XLIE2baKSK0kqsiy2kpQi6tZNvCTlw8knw7hxUK5cNP7ii+jK7YIF8eaSJO2WxVaSUsRXXyVekvKpe3eYMAEqVIjGX30Vldt58+LNJUnaJYutJEnSrnTqBJMmQaVK0XjFimi15PffjzWWJGlnack+YRAEzYCPwzAsnexzq+hbuGItsxatZN3GrVQun8YJTdM5uE6VuGNpD/nzk6SfadcOJk+OruCuXQsrV0L79jBlCjRvHnc6SdJPklJsgyAoBTQFMoB/4pXgEmfWopXck/UJc5as2ulzxzWqyZWZzTihaXoMybQn/PlJ0i9o3Toqsl26wJo1sHo1ZGZGhfe3v407nSQJCMIwzP9JgmB/YPn2c2EYBvk8Z3ZGRkZGdnZ2vrKp4P3/9u49xraqvgP49weogCIKKhSlXhK4Qo1Yr1HqK2olVusjgFVrrI3GirYqPlpNrVWsj0qtVqupVtRqY+XRVqsiBkXr9ZX66CW5FQuCD6otalQEEUFAVv/Ye8Lt7TBzz8ww+6y5n08yWefsfc6e3826M3O+e6+19plf/nZe8oGv5IYl/ivtUckpJxydJ9zn0PUrjF2i/zaOs8668fFjHjNdHbBhbduWPPzhyWXjScD99huGKj/wgdPWBdCvVWXG/3OgNQq2t0zy6+PT1yW5h2C7e/j813+Yp7zri0uGogV7VPLepx/jyt8c0X8bS+3wW3cNfrUDi9m+PTn22GFIcjLMv/3IR4YhywDMas2C7ZoMGW6tXdtaO6e1dk6S/z+WkQ3rrz958S6FoiS5oSVv/uTFN29BzET/AczonvdMtm5NDjpoeH7VVcP823PPnbQsgN2dubCs2EXfv3LROZlL+eK3LstF37/yZqqIWeg/gBW6+92TT386OeSQ4fnVVw/j/z/60WnrAtiNTR5sq2rbYl9Jjpy6Npb2+a//cF3fx9rSfwCrcLe7DeH20HHtgZ//PDnuOOEWYCKTB1v69dNrrl/X97G29B/AKh1+ePKZzySHHTY8P/jg5Kijpq0JYDe15vexnVVrbdGbwI1XbbescznM4DZ7r+y/z0rfx9rSfwBrYNOm4crtU56SvPOdN4ZcANaVT6is2EpXx7Wq7nzQfwBr5NBDhwWlAJiMocis2OaD9st9Dztgpvccc9gB2XzQfjdTRcxC/wEAsFEItqzK8x52RPbYxbtP7VHJSQ874uYtiJnoPwAANgLBllV5wOF3yGtPuMey4WiPSk454WjDWOeM/gMAYCNY8zm2rbWHrPUxmW9PvM8v5y633zdv/uTF+eIi90U95rADctLDjhCK5pT+2zi2WG4PANhNVWtt6hoWVVXbtmzZsmXbtm1Tl8IMLvr+lfn813+Yn15zfW6z9155wOF3MCezI/oPAIB1tIuT4hQeODIAAA0SSURBVJZnVWTW1OaD9hOEOqb/AADokTm2AAAAdE2wBQAAoGuGIgNsEKeeeuPjE0+crg4AgPUm2AJsEM985o2PBVsAYHdiKDIAAABdE2wBAADommALAABA1wRbAAAAuibYAgAA0DXBFgAAgK4JtgAAAHRNsAUAAKBrgi0AAABd22vqAgBYG49+9NQVAABMQ7AF2CDOOmvqCgAApmEoMgAAAF0TbAEAAOiaYAsAAEDXzLEF2CBe8YrFHwMAbHTVWpu6hkVV1bYtW7Zs2bZt29SlAHSh6sbHc/qrHQBgR7X8S3aNocgAAAB0TbAFAACga4ItAAAAXRNsAQAA6JpgCwAAQNcEWwAAALom2AIAANA1wRYAAICuCbYAAAB0ba+pCwBgbTzjGVNXAAAwDcEWYIM49dSpKwAAmIahyAAAAHRNsAUAAKBrgi0AAABdM8cWYIM48cQbH5tvCwDsTgRbgA3iHe+48bFgCwDsTgxFBgAAoGuCLQAAAF0TbAEAAOiaYAsAAEDXBFsAAAC6JtgCAADQNcEWAACArgm2AAAAdE2wBQAAoGt7TV0AAGvj5JOnrgAAYBqCLcAG8YpXTF0BAMA0DEUGAACga4ItAAAAXRNsAQAA6JpgCwAAQNcEWwAAALom2AIAANA1wRYAAICuCbYAAAB0TbAFAACga4ItAAAAXRNsAQAA6JpgCwAAQNcEWwAAALom2AIAANA1wRYAAICuCbYAAAB0TbAFAACga4ItAAAAXRNsAQAA6JpgCwAAQNcEWwAAALom2AIAANA1wRYAAICuCbYAAAB0TbAFAACga4ItAAAAXRNsAQAA6JpgCwAAQNcEWwAAALom2AIAANA1wRYAAICuCbYAAAB0TbAFAACga4ItAAAAXRNsAQAA6JpgCwAAQNcEWwAAALom2AIAANA1wRYAAICuCbYAAAB0TbAFAACga4ItAAAAXRNsAQAA6JpgCwAAQNcEWwAAALom2AIAANC1NQm2VXVUVZ1dVVdU1eVV9cGq2rQWxwYAAICl7LXaA1TVnZN8dnx6SpI9k7woyWeq6ujW2uWr/R4AAABwU1YdbJOcnOTAJA9trW1Nkqq6OMkZSU5K8so1+B4AAACwqFUNRa6qWyR5YpKvLYTa0fuTXJHkyas5PgAAACxntXNsj05y2yRf2nFja+36JNuTbK6qA1f5PQAAAOAmrTbYbhrbSxfZ972xvesqvwcAAADcpNXOsd13bK9bZN+1O71mUVW17SZ23fOCCy7Ive9975XWBgAAwJw677zz3tdaW5Ppq6sNtleN7f6L7Nt/p9fMao+rr776F+edd972Fb6f6Rw5thdOWgUrpf/6pe/6pv/6pv/6pe/6pv/6dWSSR6zVwVYbbL85tndaZN8dx/aSpQ7QWlv0kuzCldyb2s/80nd903/90nd9039903/90nd903/9WmLk7oqsdo7t+RlWP37Qjhurap8k90pyQWvtx6v8HgAAAHCTVhVsx9WPT09ySFUdu8Ou45PcKslpqzk+AAAALGe1Q5GT5FVJHp/kzKp6fZI9k7w4yXeSvGUNjg8AAAA3adXBtrV2aVU9MMkbkrwkSUvyr0me31q7YrXHBwAAgKWsxRXbtNYuTPKotTgWAAAAzKJaa1PXAAAAACu22lWRAQAAYFKCLQAAAF0TbAEAAOiaYAsAAEDXBFsAAAC6JtgCAADQtbkLtlV1VFWdXVVXVNXlVfXBqto0dV3MpqqOqKpfTF0Hs6mqI8efvyur6qqq+nhV3WPqulheVW2uqo+O/fbdqjqzqu4ydV3MrqpeVFWtqt4zdS0sraoeOvbVYl9vmro+lldVz6mqr1XVz6rqvKp6xNQ1sbyq2rrEz957pq6P5VXVXavqH6vqB1V1aVW9u6rutJpj7rVWxa2Fqrpzks+OT09JsmeSFyX5TFUd3Vq7fLLiWFZV7ZHk8CRbkrw6c3jihJtWVQcm2Zrk1kn+MsPP34uTnFtVm1trP5mwPJZQVbdJ8okkLcnJGfrwhUm2jL87r56yPnZdVd0tySunroNddvux/fMkF++07z/XuRZmVFUvS/JnSU5Ncn6SZyU5u6oe0Fr7wqTFsZxTkrxnp213SfKqJD9b92qYyXji/d+TXJPkdUluleT5SR5SVb/aWrtiJcedq2Cb4QPZgUke2lrbmiRVdXGSM5KcFH/s592dknxt6iJYsacnOSjJk1prZyRJVV2X4Y/EU5L8zYS1sbRnJzk0yT1aa+cnSVVdnuRNSR6b5MwJa2MXjScH353k2iR7T1wOu2Yh2P5Da+2CSSthJuPFlD9Nckpr7U/GbR/P8Dnm95MItnOstXbOztuq6gXjw4+vcznM7rlJ7pDkQa21zyVJVV2Y5J+SPC3D55eZzc0Vtaq6RZInJvnaQqgdvT/JFUmePEVdzOSyJI8cv74ycS3M7pix3fEPwraxPXKda2E290lyyUKoHV00tpvWvxxW6AVJ7pdhpBJ9OGBsvz9pFazEk5LcMsnbFza01i5KcscMH7rpzwkZrtYKtvNv4XPlth22rfoz59wE2yRHJ7ltki/tuLG1dn2S7Uk2j0MlmVOttWtba+eMZ9Eum7oeZvaBJC9L8uMdth0ytvpzjrXWfqu1dthOm+8+tt9Z73qYXVVtzjA64p3xoawnt09yXZInV9W3q+raqvpKVT1y6sJY1gOSXJlkv6r6QlVdM14xeoipN/2pqoOS3D/Jx1prhiLPv++N7cE7bDtkp30zm6dgu2lsL11k38I/8K7rUwrsflpr72utvbq11pKkqvbKMByrJfnQpMWxy6rql6rqSUlekuQbST48cUksYxyC/HdJfpjkDycuh9ncPsktMqxH8NYMU6rukuTD43xp5tdhGU5KfDjJl5O8NMm+Sc6sqmOWeiNz6fgMueZfpi6EXfLWJFcl+duqOrqq7pPkjUl+kORdKz3oPM2x3Xdsr1tk37U7vQa4GY0ftN+W5N5JXtdaO2/ikth170jyqAwnCY9rrf104npY3vMyXD16RGvtJ1V1wHJvYG5ckuTsJH/QWvt2klTVt5KcnmFI+e9NVxrLuE2GoeSvba29Pkmq6gtJPpch5D52wtqY3QkZMsRZUxfC8lpr26vq8RlORGwfN1+Z5NjW2opHms3TFdurxnb/Rfbtv9NrgJtJVd0qyWkZPpC9PckfT1sRM3pNhqt+v8iwovw9J66HJVTV4Rn67ANJtlfVwRnm+CXJPlV1cFXdcrICWVJr7S9aa49eCLWjT4zt3Rd7D3Nj4ULK6QsbWmufzzBH816TVMSKVNXtkjwkyVZ3UOlDVR2XIdR+LMN899/NEHDPrar7rfS48xRsvzm2i92/aOGP/CXrUwrsnqpq3yTnZFjI7dWttWctDE2mD621f2ut/VWSx2UYJmk1+fn2wCT7ZLja8N3xa2GtiSeMz+8/TWms0JVj68r7fPvR2F6z0/bLMqzWSj8em2FKgGHIHaiqPTOsJ3FxkuNba2e01t6b5DcyjNJd8V045inYnp9h9eMH7bixqvbJcObsgtbajxd7I7B64/Dj9yd5cIZhdS+buCR2QVXdoaourKo377Trq2Nrnt98Ozc3ria/8PXUcd8nxuf/MUllLKmqDqyq86tq59tSLCyA8t/rXRMzWfgduWmn7beLvuvN42I9kJ7cKcPtXS9srd2wsHFc9OuSJL+y0gPPTbAdVz8+PckhVXXsDruOz3DT3tMmKQx2HycleUSSk1trb5u6GHbZwtWF48Zh5AvuO7bfWP+S2FWttf9ZWE1+h1XlPz3uXthnVfL5dFmGD2hPqKod7zv822P70fUviRkszMV82sKGqvq1DHNvvzhJRcysqm6d5OFJvtBaW2wBWubPDzKMbDlmHCmYZFj8MslRSb610gPP0+JRyXCrg8dnWJHu9Un2zLDS4HeSvGXKwmAjG0dGvDTJ5Ukuraqn7vSSn7bW/nndC2NZrbUbquq1SV6f5FNVdUaS/ZI8J8OQntdMWR9sVK21VlWvSfKmJFur6rQMK+0+J8PVQCcI59vZST6V5NnjiYkLkrwwyfVJ/mLKwpjJbybZO4Yhd6O1dn1VvTHJy5N8rqrem2Eo+YlJbp1V/PzVvE2fq6ojk7whw5DkluGXzvNba5dMWRezqaqtSR7cWqupa2F5VbUpS58h+6/W2qZ1KYYVqarHJfmjDEN4rs5wxeHlrbXtS76RubPDz+Pft9aeOmkxLKuqnp4hEB2R4T7gH0ryktbaj5Z8I5Orqv2SvDbDfPb9M0yLe+k4coIOVNXpGUZJHNFa+/rU9bBrqqqS/E6S5ybZnOGE0lcz3Inj7BUfd96CLQAAAMxibubYAgAAwEoItgAAAHRNsAUAAKBrgi0AAABdE2wBAADommALAABA1wRbAAAAuibYAgAA0DXBFgAAgK4JtgAAAHRNsAUAAKBrgi0AAABdE2wBAADommALAABA1wRbAAAAuibYAgAA0LX/BUAUGI3l6GPZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6), dpi=144)\n",
    "\n",
    "plt.title('Decision Boundary')\n",
    "\n",
    "plt.xlim(0, 8)\n",
    "plt.ylim(0, 6)\n",
    "ax = plt.gca()                                  # gca 代表当前坐标轴，即 'get current axis'\n",
    "ax.spines['right'].set_color('none')            # 隐藏坐标轴\n",
    "ax.spines['top'].set_color('none')\n",
    "\n",
    "plt.scatter(class1[:, 0], class1[:, 1], marker='o')\n",
    "plt.scatter(class2[:, 0], class2[:, 1], marker='s')\n",
    "plt.plot([1, 5], [5, 1], '-r')\n",
    "plt.arrow(4, 4, -1, -1, shape='full', color='r')\n",
    "plt.plot([3, 3], [0.5, 6], '--b')\n",
    "plt.arrow(4, 4, -1, 0, shape='full', color='b', linestyle='--')\n",
    "plt.annotate(r'margin 1',\n",
    "             xy=(3.5, 4), xycoords='data',\n",
    "             xytext=(3.1, 4.5), fontsize=10,\n",
    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.annotate(r'margin 2',\n",
    "             xy=(3.5, 3.5), xycoords='data',\n",
    "             xytext=(4, 3.5), fontsize=10,\n",
    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.annotate(r'support vector',\n",
    "             xy=(4, 4), xycoords='data',\n",
    "             xytext=(5, 4.5), fontsize=10,\n",
    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.annotate(r'support vector',\n",
    "             xy=(2, 2), xycoords='data',\n",
    "             xytext=(0.5, 1.5), fontsize=10,\n",
    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-27T05:39:25.250684Z",
     "start_time": "2020-11-27T05:39:24.546623Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(5, 4.5, 'A')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6), dpi=144)\n",
    "\n",
    "plt.title('Linear Support Vector Machine in linearly separable case')\n",
    "\n",
    "plt.xlim(0, 8)\n",
    "plt.ylim(0, 6)\n",
    "ax = plt.gca()                                  # gca 代表当前坐标轴，即 'get current axis'\n",
    "ax.spines['right'].set_color('none')            # 隐藏坐标轴\n",
    "ax.spines['top'].set_color('none')\n",
    "\n",
    "plt.scatter(class1[:, 0], class1[:, 1], marker='o')\n",
    "plt.scatter(class2[:, 0], class2[:, 1], marker='s')\n",
    "plt.plot([1, 5], [5, 1], '-r')\n",
    "plt.plot([0, 4], [4, 0], '--b', [2, 6], [6, 2], '--b')\n",
    "plt.arrow(4, 4, -1, -1, shape='full', color='b')\n",
    "plt.annotate(r'$w^T x + b = 0$',\n",
    "             xy=(5, 1), xycoords='data',\n",
    "             xytext=(6, 1), fontsize=10,\n",
    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.annotate(r'$w^T x + b = 1$',\n",
    "             xy=(6, 2), xycoords='data',\n",
    "             xytext=(7, 2), fontsize=10,\n",
    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.annotate(r'$w^T x + b = -1$',\n",
    "             xy=(3.5, 0.5), xycoords='data',\n",
    "             xytext=(4.5, 0.2), fontsize=10,\n",
    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.annotate(r'd',\n",
    "             xy=(3.5, 3.5), xycoords='data',\n",
    "             xytext=(2, 4.5), fontsize=10,\n",
    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "plt.annotate(r'A',\n",
    "             xy=(4, 4), xycoords='data',\n",
    "             xytext=(5, 4.5), fontsize=10,\n",
    "             arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分离超平面：$w\\cdot x+b=0$ 或者 $w^Tx+b=0$\n",
    "\n",
    "\n",
    "\n",
    "给定分离超平面$(w,b)$，样本可表示为：\n",
    "\n",
    "$w^Tx+b\\ \\geq+1$\n",
    "\n",
    "$w^Tx+b\\ \\leq-1$\n",
    "\n",
    "### 间隔(margin)\n",
    "\n",
    "一般说来，一个点距离分离超平面的远近可以表示分类预测的确信程度。在超平面$w\\cdot x+b=0$确定的确定的情况下，可以用量$y_i(w\\cdot x+b)$来表示对$(x,y)$分类点正确性和确信度，这就是：\n",
    "\n",
    "**函数间隔**：$\\hat{\\gamma_i} = y_i(w\\cdot x+b)\\ or\\ y_i(w^Tx+b)$ \n",
    "\n",
    "选择分离超平面时，$\\hat{\\gamma_i}$还不够。因为只要成比例改变$w$和$b$，超平面并没有变化，但$\\hat{\\gamma_i}$确是原来的几倍。所以要对法向量$w$加某些约束，如规范化。\n",
    "\n",
    "**几何间隔**：$\\gamma_i = \\frac{y_i(w^Tx_i+b)}{||w||}$，\n",
    "\n",
    "当数据$(x_i,y_i)$被正确分类时，几何间隔就是点$(x_i)$到超平面的距离.点到超平面的远近可以表示分类预测到确信程度——越远表示越确信，越近就表示不那么确信。\n",
    "\n",
    "**点$x$到超平面$(w,b)$的距离**\n",
    "\n",
    "点$x$到超平面$(w,b)$的垂直距离：$\\gamma=\\frac{|w \\cdot x+b|}{||w||}$ 或者 $\\gamma=\\frac{|w^Tx+b|}{||w||_2}$\n",
    "\n",
    "其中$||w||$和$||w||_2$都为2-范数：$||w||_2=\\sqrt[2]{\\sum^m_{l=1}{w^{(l)}}^2}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线性可分SVM\n",
    "\n",
    "SVM的基本思想是求解能够正确划分数据集并且几何间隔最大的分类超平面。\n",
    "\n",
    "为了求几何间隔最大，SVM基本问题可以转化为求解:\n",
    "\n",
    "$$\\max_{w,b}\\ \\gamma$$\n",
    "\n",
    "$$(subject\\ to)\\ y_i\\left(\\frac{{w^T}x_i+{b}}{||w||}\\right)\\geq {\\gamma},\\ i=1,2,..,N$$\n",
    "\n",
    "其中$\\gamma=\\min _{i=1, N} \\gamma_{i}$为几何间隔。分类点几何间隔最大，同时被正确分类。\n",
    "\n",
    "但这个方程并非凸函数求解，所以要先①将方程转化为凸函数，②用拉格朗日乘子法和KKT条件求解对偶问题。\n",
    "\n",
    "①转化为凸函数：\n",
    "\n",
    "先令${\\gamma}=1$，方便计算（参照衡量，不影响评价结果）\n",
    "\n",
    "$$\\max_{w,b}\\ \\frac{1}{||w||}$$\n",
    "\n",
    "$$s.t.\\ y_i({w^T}x_i+{b})\\geq {1},\\ i=1,2,..,N$$\n",
    "\n",
    "再将$\\max_{w,b}\\ \\frac{1}{||w||}$转化成$\\min_{w,b}\\ \\frac{1}{2}||w||^2$求解凸函数，1/2是为了求导之后方便计算。\n",
    "\n",
    "$$\\min_{w,b}\\ \\frac{1}{2}||w||^2$$\n",
    "\n",
    "$$s.t.\\ y_i(w^Tx_i+b)\\geq 1,\\ i=1,2,..,N$$\n",
    "\n",
    "②用拉格朗日乘子法和KKT条件求解最优值：\n",
    "\n",
    "$$\\min_{w,b}\\ \\frac{1}{2}||w||^2$$\n",
    "\n",
    "$$s.t.\\ -y_i(w^Tx_i+b)+1\\leq 0,\\ i=1,2,..,m$$\n",
    "\n",
    "整合成：\n",
    "\n",
    "$$L(w, b, \\alpha) = \\frac{1}{2}||w||^2+\\sum^m_{i=1}\\alpha_i(-y_i(w^Tx_i+b)+1)$$\n",
    "\n",
    "推导：$\\min\\ f(x)=\\min_{w,b} \\max_\\alpha\\ L(w, b, \\alpha)\\geq \\max_\\alpha \\min_{w,b}\\ L(w, b, \\alpha)$\n",
    "\n",
    "根据KKT条件：\n",
    "\n",
    "$$\\frac{\\partial }{\\partial w}L(w, b, \\alpha)=w-\\sum\\alpha_iy_ix_i=0,\\ w=\\sum\\alpha_iy_ix_i$$\n",
    "\n",
    "$$\\frac{\\partial }{\\partial b}L(w, b, \\alpha)=\\sum\\alpha_iy_i=0$$\n",
    "\n",
    "代入$ L(w, b, \\alpha)$\n",
    "\n",
    "$\\min_{w,b}\\  L(w, b, \\alpha)=\\frac{1}{2}||w||^2+\\sum^m_{i=1}\\alpha_i(-y_i(w^Tx_i+b)+1)$\n",
    "\n",
    "$\\qquad\\qquad\\qquad=\\frac{1}{2}w^Tw-\\sum^m_{i=1}\\alpha_iy_iw^Tx_i-b\\sum^m_{i=1}\\alpha_iy_i+\\sum^m_{i=1}\\alpha_i$\n",
    "\n",
    "$\\qquad\\qquad\\qquad=\\frac{1}{2}w^T\\sum\\alpha_iy_ix_i-\\sum^m_{i=1}\\alpha_iy_iw^Tx_i+\\sum^m_{i=1}\\alpha_i$\n",
    "\n",
    "$\\qquad\\qquad\\qquad=\\sum^m_{i=1}\\alpha_i-\\frac{1}{2}\\sum^m_{i=1}\\alpha_iy_iw^Tx_i$\n",
    "\n",
    "$\\qquad\\qquad\\qquad=\\sum^m_{i=1}\\alpha_i-\\frac{1}{2}\\sum^m_{i,j=1}\\alpha_i\\alpha_jy_iy_j(x_ix_j)$\n",
    "\n",
    "再把max问题转成min问题：\n",
    "\n",
    "$\\max_\\alpha\\ \\sum^m_{i=1}\\alpha_i-\\frac{1}{2}\\sum^m_{i,j=1}\\alpha_i\\alpha_jy_iy_j(x_i\\cdot x_j)=\\min_\\alpha \\frac{1}{2}\\sum^m_{i,j=1}\\alpha_i\\alpha_jy_iy_j(x_i\\cdot x_j)-\\sum^m_{i=1}\\alpha_i$\n",
    "\n",
    "$s.t.\\ \\sum^m_{i=1}\\alpha_iy_i=0,$\n",
    "\n",
    "$ \\alpha_i \\geq 0,i=1,2,...,m$\n",
    "\n",
    "以上为SVM对偶问题的对偶形式\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 课本例子7.2\n",
    "\n",
    "已知正例点$x_1,x_2$，负例点$x_3$，试求最大间隔分离超平面。\n",
    "$$\\frac{1}{2}(18\\alpha^2_1 + 42\\alpha_1\\alpha_2 - 12\\alpha_1\\alpha_3 + 25\\alpha^2_2 - 14\\alpha_2\\alpha_3 + 2\\alpha^2_3) - (\\alpha_1+\\alpha_2+\\alpha_3)$$\n",
    "$$\\alpha_1 + \\alpha_2 - \\alpha_3 = 0$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T00:12:50.318337Z",
     "start_time": "2020-11-18T00:12:50.312269Z"
    }
   },
   "outputs": [],
   "source": [
    "#x1,x2,x3\n",
    "X = np.array([[3, 3], [4, 3], [1, 1]])\n",
    "y = np.array([1, 1, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T00:12:51.207856Z",
     "start_time": "2020-11-18T00:12:50.894921Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     fun: 0.2500000000000011\n",
      "     jac: array([0.50000001, 0.50000001, 0.        ])\n",
      " message: 'Optimization terminated successfully.'\n",
      "    nfev: 10\n",
      "     nit: 2\n",
      "    njev: 2\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([ 0.5,  0.5, -2. ])\n"
     ]
    }
   ],
   "source": [
    "from scipy import optimize as opti\n",
    "import numpy as np\n",
    "\n",
    "fun = lambda w: ((w[0]) ** 2 + (w[1]) ** 2)/2\n",
    "cons = ({'type': 'ineq', 'fun': lambda w: X[0][0] * w[0] + X[0][1] * w[1] + w[2] - 1},\n",
    "    {'type': 'ineq', 'fun': lambda w: X[1][0] * w[0] + X[1][1] * w[1] + w[2] - 1},\n",
    "    {'type': 'ineq', 'fun': lambda w: -X[2][0]* w[0] - X[2][1]* w[1] - w[2] - 1})\n",
    "res = opti.minimize(fun, np.ones(3), method='SLSQP', constraints=cons)\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 课本习题1.2\n",
    "\n",
    "已知正例点$x_1,x_2,x_3$，负例点$x_4,x_5$，试求最大间隔分离超平面。\n",
    "$$\\frac{1}{2}(10\\alpha^2_1 + 16\\alpha_1\\alpha_2 + 18\\alpha_1\\alpha_3 - 8\\alpha_1\\alpha_4 - 14\\alpha_1\\alpha_5 + 26\\alpha^2_2 + 30\\alpha_2\\alpha_3 - 14\\alpha_2\\alpha_4 - 24\\alpha_2\\alpha_5 + 36\\alpha^2_3 - 18\\alpha_3\\alpha_4 - 30\\alpha_3\\alpha_5 + 10\\alpha^2_4 + 16\\alpha_4\\alpha_5 + 26\\alpha^2_5) - (\\alpha_1+\\alpha_2+\\alpha_3+\\alpha_4+\\alpha_5)$$\n",
    "$$\\alpha_1 + \\alpha_2 + \\alpha_3 - \\alpha_4 - \\alpha_5 = 0$$\n",
    "\n",
    "Alpha: [0.5 0.  2.  0.  2.5]\n",
    "\n",
    "Hyperplane: hyperplane: $(-1.0, 2.0)^T\\cdot x -2.0 = 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-27T08:40:39.520097Z",
     "start_time": "2020-11-27T08:40:39.515725Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#x1,x2,x3,x4,x5\n",
    "X = np.array([[1, 2], [2, 3], [3, 3], [2, 1], [3, 2]])\n",
    "y = np.array([1, 1, 1, -1, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-27T08:40:40.751027Z",
     "start_time": "2020-11-27T08:40:40.742323Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def problem(X, y, debug=False):\n",
    "    f = \"+\".join([\"(w[{}]) ** 2\".format(i) for i in range(X.shape[1])])\n",
    "    f = \"lambda w: ({})/2\".format(f)\n",
    "    if debug:\n",
    "        print(\"Objective: {}\".format(f))\n",
    "    fun = eval(f)\n",
    "    c=[]\n",
    "    for i,_ in enumerate(y):\n",
    "        f = [\"{}*w[{}]\".format(X[i][l],l) for l in range(X.shape[1])]\n",
    "        f.append(\"w[{}]\".format(X.shape[1]))\n",
    "        if y[i]==1:\n",
    "            f = \"lambda w: \" + \"+\".join(f) + \"-1\"\n",
    "        else:\n",
    "            f.append(\"1\")\n",
    "            f = \"lambda w: -\" + \"-\".join(f)\n",
    "        if debug:\n",
    "            print(\"Cons: {}\".format(f))\n",
    "        c.append({'type': 'ineq', 'fun': eval(f)})\n",
    "    cons = tuple(c)          \n",
    "    return fun, cons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-27T08:40:41.863455Z",
     "start_time": "2020-11-27T08:40:41.854940Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     fun: 2.5000000000000098\n",
      "     jac: array([-1.,  2.,  0.,  0.,  0.])\n",
      " message: 'Optimization terminated successfully.'\n",
      "    nfev: 28\n",
      "     nit: 4\n",
      "    njev: 4\n",
      "  status: 0\n",
      " success: True\n",
      "       x: array([-1.,  2., -2.,  1.,  1.])\n"
     ]
    }
   ],
   "source": [
    "from scipy import optimize as opti\n",
    "fun,cons = problem(X, y, debug=False)\n",
    "res = opti.minimize(fun, np.ones(X.shape[0]), method='SLSQP', constraints=cons)\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对偶问题\n",
    "\n",
    "线性可分支持向量机的最优解存在且唯一。位于间隔边界上的实例点为支持向量。最优分离超平面由支持向量完全决定。\n",
    "二次规划问题的对偶问题是\n",
    "$$\\min_\\alpha \\frac{1}{2} \\sum_{i=1}^{N} \\sum_{j=1}^{N} \\alpha_{i} \\alpha_{j} y_{i} y_{j}\\left(x_{i} \\cdot x_{j}\\right)-\\sum_{i=1}^{N} \\alpha_{i}$$\n",
    "\n",
    "$$s.t. \\quad \\sum_{i=1}^{N} \\alpha_{i} y_{i}=0$$\n",
    "\n",
    "$$\\alpha_{i} \\geqslant 0, \\quad i=1,2, \\cdots, N$$\n",
    "\n",
    "通常，通过求解对偶问题学习线性可分支持向量机，即首先求解对偶问题的最优值$\\alpha^*$，然后求最优值$w^*$和$b^*$，得出分离超平面和分类决策函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T00:12:59.528277Z",
     "start_time": "2020-11-18T00:12:59.506557Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def dualProblem(X, y, latex=True, debug=False):\n",
    "    Gram = np.dot(X, X.T)\n",
    "    if latex:\n",
    "        print(Gram.round(2))\n",
    "    if latex:\n",
    "        obj = \"\\\\frac{1}{2}(\"\n",
    "        idx = 1\n",
    "    else:\n",
    "        obj = \".5*(\"\n",
    "        idx = 0\n",
    "    for i in range(X.shape[0]):\n",
    "        for j in range(i, X.shape[0]):\n",
    "            if latex:\n",
    "                obj_ii = \"{}\\\\alpha^2_{}\"\n",
    "                obj_ij = \"{}\\\\alpha_{}\\\\alpha_{}\"\n",
    "            else:\n",
    "                obj_ii = \"{}*(a[{}])**2\"\n",
    "                obj_ij = \"{}*a[{}]*a[{}]\"\n",
    "            if i==j:\n",
    "                obj_ii = obj_ii.format(Gram[i,i], i+idx)\n",
    "                if i==0:\n",
    "                    obj += obj_ii\n",
    "                else:\n",
    "                    obj += \" + \"+obj_ii\n",
    "            elif y[i]*y[j]>0:\n",
    "                obj_ij = obj_ij.format(2*Gram[i,j], i+idx, j+idx)\n",
    "                obj += \" + \"+obj_ij\n",
    "            else:\n",
    "                obj_ij = obj_ij.format(2*Gram[i,j], i+idx, j+idx)\n",
    "                obj += \" - \"+obj_ij\n",
    "    obj += \") - (\"\n",
    "    if latex:\n",
    "        alpha_i = \"\\\\alpha_{}\"\n",
    "    else:\n",
    "        alpha_i = \"a[{}]\"\n",
    "    for i in range(y.shape[0]):\n",
    "        obj += alpha_i.format(i+idx)\n",
    "        if (i+1)<y.shape[0]:\n",
    "            obj += \"+\"\n",
    "    obj += \")\"\n",
    "    if latex:\n",
    "        print(obj)\n",
    "    else:\n",
    "        obj = \"lambda a: \"+ obj\n",
    "        if debug:\n",
    "            print(\"Objective: {}\".format(obj))\n",
    "        fun = eval(obj)\n",
    "        \n",
    "    cons=[]\n",
    "    cstr = \"\"\n",
    "    for i in range(y.shape[0]):\n",
    "        if y[i]==-1:\n",
    "            cstr += \" - \"+alpha_i.format(i+idx)\n",
    "        else:\n",
    "            if i!=0:\n",
    "                cstr += \" + \"\n",
    "            cstr += alpha_i.format(i+idx)\n",
    " \n",
    "    if latex:\n",
    "        cstr += \" = 0\"\n",
    "        print(cstr)\n",
    "        return obj, cstr\n",
    "    else:\n",
    "        cstr = \"lambda a: \"+cstr\n",
    "        if debug:\n",
    "            print(\"Cons: {}\".format(cstr))\n",
    "        cons.append({'type': 'eq', 'fun': eval(cstr)})\n",
    "        for i in range(y.shape[0]):\n",
    "            cstr = \"lambda a: a[{}]\".format(i)\n",
    "            cons.append({'type': 'ineq', 'fun': eval(cstr)})\n",
    "            if debug:\n",
    "                print(\"Cons: {}\".format(cstr))\n",
    "        return fun, tuple(cons)\n",
    "    \n",
    "def hyperplane(alpha, X, y):\n",
    "    Gram = np.dot(X, X.T)\n",
    "    yx = y.reshape(-1, 1) * X\n",
    "    w = np.dot(yx.T, alpha)\n",
    "    j = [j for j,v in enumerate(alpha) if alpha[j]>0][0]\n",
    "    b = y[j] - sum([alpha[i]*y[i]*Gram[i,j] for i in range(y.shape[0])])\n",
    "    if b > 0:\n",
    "        msg = \"{}^T\\cdot x + {:.1f} = 0\"\n",
    "    else:\n",
    "        msg = \"{}^T\\cdot x {:.1f} = 0\"   \n",
    "    return msg.format(tuple(w.round(2)), b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T00:13:01.420107Z",
     "start_time": "2020-11-18T00:13:01.412169Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5  8  9  4  7]\n",
      " [ 8 13 15  7 12]\n",
      " [ 9 15 18  9 15]\n",
      " [ 4  7  9  5  8]\n",
      " [ 7 12 15  8 13]]\n",
      "\\frac{1}{2}(5\\alpha^2_1 + 16\\alpha_1\\alpha_2 + 18\\alpha_1\\alpha_3 - 8\\alpha_1\\alpha_4 - 14\\alpha_1\\alpha_5 + 13\\alpha^2_2 + 30\\alpha_2\\alpha_3 - 14\\alpha_2\\alpha_4 - 24\\alpha_2\\alpha_5 + 18\\alpha^2_3 - 18\\alpha_3\\alpha_4 - 30\\alpha_3\\alpha_5 + 5\\alpha^2_4 + 16\\alpha_4\\alpha_5 + 13\\alpha^2_5) - (\\alpha_1+\\alpha_2+\\alpha_3+\\alpha_4+\\alpha_5)\n",
      "\\alpha_1 + \\alpha_2 + \\alpha_3 - \\alpha_4 - \\alpha_5 = 0\n"
     ]
    }
   ],
   "source": [
    "from scipy import optimize as opti\n",
    "latex=True\n",
    "obj,cons = dualProblem(X, y, latex=latex, debug=True)\n",
    "if not latex:\n",
    "    res = opti.minimize(obj, np.ones(X.shape[0]), method='SLSQP', constraints=cons)\n",
    "    alpha = res[\"x\"].round(2)\n",
    "    print(\"Alpha: {}\".format(alpha))\n",
    "    print(\"Hyperplane: {}\".format(hyperplane(alpha, X, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T00:13:03.841983Z",
     "start_time": "2020-11-18T00:13:03.821175Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective: lambda a: .5*(65.0*(a[0])**2 + 124.0*a[0]*a[1] - 186.0*a[0]*a[2] + 56.0*a[0]*a[3] - 248.0*a[0]*a[4] + 61.0*(a[1])**2 - 150.0*a[1]*a[2] + 48.0*a[1]*a[3] - 222.0*a[1]*a[4] + 234.0*(a[2])**2 - 120.0*a[2]*a[3] + 462.0*a[2]*a[4] + 16.0*(a[3])**2 - 128.0*a[3]*a[4] + 265.0*(a[4])**2) - (a[0]+a[1]+a[2]+a[3]+a[4])\n",
      "Cons: lambda a:  - a[0] - a[1] + a[2] - a[3] + a[4]\n",
      "Cons: lambda a: a[0]\n",
      "Cons: lambda a: a[1]\n",
      "Cons: lambda a: a[2]\n",
      "Cons: lambda a: a[3]\n",
      "Cons: lambda a: a[4]\n",
      "Alpha: [ 0.02 -0.    0.   -0.    0.02]\n",
      "Hyperplane: (0.18, -0.02)^T\\cdot x -2.2 = 0\n"
     ]
    }
   ],
   "source": [
    "from scipy import optimize as opti\n",
    "import numpy as np\n",
    "latex=False\n",
    "T = np.loadtxt(\"7ex1.2.txt\").round(2)\n",
    "X = T[:,:-1]\n",
    "y = T[:,2]\n",
    "obj,cons = dualProblem(X, y, latex=latex, debug=True)\n",
    "if not latex:\n",
    "    res = opti.minimize(obj, np.ones(X.shape[0]), method='SLSQP', constraints=cons)\n",
    "    alpha = res[\"x\"].round(2)\n",
    "    print(\"Alpha: {}\".format(alpha))\n",
    "    print(\"Hyperplane: {}\".format(hyperplane(alpha, X, y)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线性SVM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T03:14:46.119049Z",
     "start_time": "2020-11-18T03:14:46.103995Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "    \n",
    "def plot_hyperplane(clf, X, y, \n",
    "                    h=0.02, \n",
    "                    draw_sv=True, \n",
    "                    title='hyperplan'):\n",
    "    \n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "\n",
    "    plt.title(title)\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "    plt.xticks(())\n",
    "    plt.yticks(())\n",
    "    \n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.5)\n",
    "\n",
    "    markers = ['o', 's', '^']\n",
    "    colors = ['b', 'r', 'c']\n",
    "    labels = np.unique(y)\n",
    "    for label in labels:\n",
    "        plt.scatter(X[y==label][:, 0], \n",
    "                    X[y==label][:, 1], \n",
    "                    c=colors[label], \n",
    "                    marker=markers[label])\n",
    "    if draw_sv:\n",
    "        sv = clf.support_vectors_\n",
    "        plt.scatter(sv[:, 0], sv[:, 1], c='w', marker='x') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T03:14:48.005446Z",
     "start_time": "2020-11-18T03:14:46.957508Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rchen/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "X, y = make_blobs(n_samples=100, centers=3, \n",
    "                  random_state=0, cluster_std=0.8)\n",
    "clf_linear = svm.SVC(C=1.0, kernel='linear')\n",
    "clf_poly = svm.SVC(C=1.0, kernel='poly', degree=3)\n",
    "clf_rbf = svm.SVC(C=1.0, kernel='rbf', gamma=0.5)\n",
    "clf_rbf2 = svm.SVC(C=1.0, kernel='rbf', gamma=0.1)\n",
    "\n",
    "plt.figure(figsize=(10, 10), dpi=144)\n",
    "\n",
    "clfs = [clf_linear, clf_poly, clf_rbf, clf_rbf2]\n",
    "titles = ['Linear Kernel', \n",
    "          'Polynomial Kernel with Degree=3', \n",
    "          'Gaussian Kernel with $\\gamma=0.5$', \n",
    "          'Gaussian Kernel with $\\gamma=0.1$']\n",
    "for clf, i in zip(clfs, range(len(clfs))):\n",
    "    clf.fit(X, y)\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plot_hyperplane(clf, X, y, title=titles[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### kernel\n",
    "\n",
    "在低维空间计算获得高维空间的计算结果，也就是说计算结果满足高维（满足高维，才能说明高维下线性可分）。\n",
    "\n",
    "### soft margin & slack variable\n",
    "\n",
    "引入松弛变量$\\xi\\geq0$，对应数据点允许偏离的functional margin 的量。\n",
    "\n",
    "目标函数：\n",
    "\n",
    "$$\\min\\ \\frac{1}{2}||w||^2+C\\sum\\xi_i\\qquad s.t.\\ y_i(w^Tx_i+b)\\geq1-\\xi_i$$ \n",
    "\n",
    "对偶问题：\n",
    "\n",
    "$$\\max\\ \\sum^m_{i=1}\\alpha_i-\\frac{1}{2}\\sum^m_{i,j=1}\\alpha_i\\alpha_jy_iy_j(x_ix_j)=\\min \\frac{1}{2}\\sum^m_{i,j=1}\\alpha_i\\alpha_jy_iy_j(x_ix_j)-\\sum^m_{i=1}\\alpha_i$$\n",
    "\n",
    "$$s.t.\\ C\\geq\\alpha_i \\geq 0,i=1,2,...,m\\quad \\sum^m_{i=1}\\alpha_iy_i=0,$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 序列最小最优化SMO\n",
    "\n",
    "Sequential Minimal Optimization\n",
    "\n",
    "首先定义特征到结果的输出函数：$u=w^Tx+b$.\n",
    "\n",
    "因为$w=\\sum\\alpha_iy_ix_i$\n",
    "\n",
    "有$u=\\sum y_i\\alpha_iK(x_i, x)-b$\n",
    "\n",
    "\n",
    "----\n",
    "\n",
    "$$\\max \\sum^m_{i=1}\\alpha_i-\\frac{1}{2}\\sum^m_{i=1}\\sum^m_{j=1}\\alpha_i\\alpha_jy_iy_j<\\phi(x_i)^T,\\phi(x_j)>$$\n",
    "\n",
    "$$s.t.\\ \\sum^m_{i=1}\\alpha_iy_i=0,$$\n",
    "\n",
    "$$ \\alpha_i \\geq 0,i=1,2,...,m$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T01:08:56.074841Z",
     "start_time": "2020-11-18T01:08:56.035227Z"
    }
   },
   "outputs": [],
   "source": [
    "class SVM:\n",
    "    def __init__(self,\n",
    "                 tol=10e-3,\n",
    "                 C=0.6,\n",
    "                 n_iters=10,\n",
    "                 verbose=True):\n",
    "        self.alpha = None\n",
    "        self.b = 0\n",
    "        self.tol = tol\n",
    "        self.C = C\n",
    "        self.n_iters = n_iters\n",
    "        self.m = 0\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.m, self.n = X.shape\n",
    "        #1. 创建一个 alpha 向量并将其初始化为0向量\n",
    "        self.alpha = np.zeros(self.m)\n",
    "        self.b = 0\n",
    "        self.X = X\n",
    "        self.Y = y\n",
    "        self._do_smo(X, y)\n",
    "\n",
    "    def _do_smo(self, X, y):\n",
    "        n_iter = 0\n",
    "        #2. 当迭代次数小于最大迭代次数时(外循环)\n",
    "        while n_iter < self.n_iters:\n",
    "            alpha_pairs_changed = 0\n",
    "            #3. 对数据集中的每个数据向量(内循环)：\n",
    "            for i in range(self.m):\n",
    "                ei = self._do_ei(X, y, i)\n",
    "                #4. 如果该数据向量可以被优化\n",
    "                if ((y[i] * ei < -self.tol) and (self.alpha[i] < self.C)) or \\\n",
    "                   ((y[i] * ei > self.tol) and (self.alpha[i] > 0)):\n",
    "                    #5. 随机选择另外一个数据向量\n",
    "                    j = self._do_selectj(i, self.m)\n",
    "                    ej = self._do_ei(X, y, j)\n",
    "                    #6. 同时优化这两个向量\n",
    "                    alphaiold = self.alpha[i].copy()\n",
    "                    alphajold = self.alpha[j].copy()\n",
    "                    if y[i] != y[j]:\n",
    "                        L = max(0.0, self.alpha[j] - self.alpha[i])\n",
    "                        H = min(self.C, self.C + self.alpha[j] - self.alpha[i])\n",
    "                    else:\n",
    "                        L = max(0.0, self.alpha[j] + self.alpha[i] - self.C)\n",
    "                        H = min(self.C, self.alpha[j] + self.alpha[i])\n",
    "                    if L == H:\n",
    "                        if self.verbose:\n",
    "                            print(\"L==H\")\n",
    "                        continue\n",
    "                    eta = self._do_eta(X, i, j)\n",
    "                    # 简化处理\n",
    "                    if eta >= 0:\n",
    "                        if self.verbose:\n",
    "                            print(\"eta>=0\")\n",
    "                        continue\n",
    "                    # alpha[j]\n",
    "                    self.alpha[j] -= y[j] * (ei - ej) / eta\n",
    "                    self.alpha[j] = self._do_clipalpha(self.alpha[j], H, L)\n",
    "\n",
    "                    if abs(self.alpha[j] - alphajold) < 0.00001:\n",
    "                        if self.verbose:\n",
    "                            print(\"j not moving enough\")\n",
    "                        continue\n",
    "                    # alpha[i]\n",
    "                    self.alpha[i] += y[j] * y[i] * (alphajold - self.alpha[j])\n",
    "                    #\n",
    "                    b1 = self.b - ei - y[i] * (self.alpha[i] - alphaiold) * np.dot(X[i, :], X[i, :]) - \\\n",
    "                         y[j] * (self.alpha[j] - alphajold) * np.dot(X[i, :], X[j, :])\n",
    "\n",
    "                    b2 = self.b - ej - y[i] * (self.alpha[i] - alphaiold) * np.dot(X[i, :], X[j, :]) - \\\n",
    "                         y[j] * (self.alpha[j] - alphajold) * np.dot(X[j, :], X[j, :])\n",
    "\n",
    "                    if (0 < self.alpha[i]) and (self.C > self.alpha[j]):\n",
    "                        self.b = b1\n",
    "                    elif (0 < self.alpha[j]) and (self.C > self.alpha[j]):\n",
    "                        self.b = b2\n",
    "                    else:\n",
    "                        self.b = (b1 + b2) / 2\n",
    "                    alpha_pairs_changed += 1\n",
    "                    if self.verbose:\n",
    "                        print(\"iter: %d i: %d, paris changed %d\" % (n_iter, i, alpha_pairs_changed))\n",
    "                #7. 如果两个向量都不能被优化，退出内循环\n",
    "            if alpha_pairs_changed == 0:\n",
    "                n_iter += 1\n",
    "            else:\n",
    "                n_iter = 0\n",
    "            if self.verbose:\n",
    "                print(\"iteration number: %d\" % n_iter)\n",
    "            #8. 如果所有向量都没被优化，增加迭代数目，继续下一次循环\n",
    "        return self.alpha, self.b\n",
    "\n",
    "    def _do_smop(self):\n",
    "        return self.alpha, self.b\n",
    "\n",
    "    def _do_gxi(self, X, y, i):\n",
    "        gxi = np.sum(self.alpha*y*np.dot(X, X[i, :]), axis=0) + self.b\n",
    "        return gxi\n",
    "\n",
    "    def _do_ei(self, X, y, i):\n",
    "        ei = self._do_gxi(X, y, i) - y[i]\n",
    "        return ei\n",
    "\n",
    "    def _do_eta(self, X, i, j):\n",
    "        eta = 2 * np.dot(X[i, :], X[j, :]) - np.dot(X[i, :], X[i, :]) - np.dot(X[j, :], X[j, :])\n",
    "        return eta\n",
    "\n",
    "    def _do_selectj(self, i, m):\n",
    "        j = i\n",
    "        while j == i:\n",
    "            j = int(np.random.uniform(0, m))\n",
    "        return j\n",
    "\n",
    "    # todo: use numpy clip\n",
    "    def _do_clipalpha(self, alpha, H, L):\n",
    "        if alpha > H:\n",
    "            alpha = H\n",
    "        if L > alpha:\n",
    "            alpha = L\n",
    "        return alpha\n",
    "\n",
    "    def predict(self, x_test):\n",
    "        r = self.b\n",
    "        for i in range(self.m):\n",
    "            r += self.alpha[i] * self.Y[i] * sum([x_test[k] * self.X[i][k] for k in range(self.n)])\n",
    "\n",
    "        return 1 if r > 0 else -1\n",
    "\n",
    "    def score(self, X_test, y_test):\n",
    "        right_count = 0\n",
    "        for i in range(len(X_test)):\n",
    "            result = self.predict(X_test[i])\n",
    "            if result == y_test[i]:\n",
    "                right_count += 1\n",
    "        return right_count / len(X_test)\n",
    "\n",
    "    def _weight(self):\n",
    "        # linear model\n",
    "        yx = self.Y.reshape(-1, 1) * self.X\n",
    "        self.w = np.dot(yx.T, self.alpha)\n",
    "        return self.w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### iris数据集二分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T01:08:59.328395Z",
     "start_time": "2020-11-18T01:08:59.319809Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import  train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T01:09:00.778229Z",
     "start_time": "2020-11-18T01:09:00.770285Z"
    }
   },
   "outputs": [],
   "source": [
    "def create_data(verbose=False):\n",
    "    iris = load_iris()\n",
    "    df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "    df['label'] = iris.target\n",
    "    df.columns = [\n",
    "        'sepal length', 'sepal width', 'petal length', 'petal width', 'label'\n",
    "    ]\n",
    "    data = np.array(df.iloc[:100, [0, 1, -1]])\n",
    "    for i in range(len(data)):\n",
    "        if data[i, -1] == 0:\n",
    "            data[i, -1] = -1\n",
    "    if verbose:\n",
    "        print(data[:5])\n",
    "    return data[:, :2], data[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T01:09:02.265831Z",
     "start_time": "2020-11-18T01:09:02.253830Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.1  3.5 -1. ]\n",
      " [ 4.9  3.  -1. ]\n",
      " [ 4.7  3.2 -1. ]\n",
      " [ 4.6  3.1 -1. ]\n",
      " [ 5.   3.6 -1. ]]\n"
     ]
    }
   ],
   "source": [
    "X, y = create_data(verbose=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T01:09:03.701230Z",
     "start_time": "2020-11-18T01:09:03.597472Z"
    }
   },
   "outputs": [],
   "source": [
    "svm = SVM(n_iters=50,verbose=False)\n",
    "svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T01:09:04.423781Z",
     "start_time": "2020-11-18T01:09:04.410662Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T01:09:06.649616Z",
     "start_time": "2020-11-18T01:09:06.431935Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [ 1.82 -1.82] b: -4.29\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 中文标题\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.title('鸢尾花线性数据示例')\n",
    "# 画点\n",
    "plt.scatter(X[:50, 0], X[:50, 1], c='b', label='Iris-setosa',)\n",
    "plt.scatter(X[50:100, 0], X[50:100, 1], c='orange', label='Iris-versicolor')\n",
    "# 画感知机的线\n",
    "x_ponits = np.arange(4, 8)\n",
    "svm._weight()\n",
    "y_ = -(svm.w[0]*x_ponits + svm.b)/svm.w[1]\n",
    "plt.plot(x_ponits, y_)\n",
    "# 画图例等\n",
    "plt.legend()  # 显示图例\n",
    "plt.grid(False)  # 不显示网格\n",
    "plt.xlabel('sepal length (cm)')\n",
    "plt.ylabel('sepal width (cm)')\n",
    "\n",
    "#超平面\n",
    "print(\"w: {} b: {}\".format(svm.w.round(2), svm.b.round(2)))\n",
    "# svm.w = [ 70.7, -87.9]\n",
    "# svm.b = [-117.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-learn实例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T01:00:45.444887Z",
     "start_time": "2020-11-18T01:00:45.432852Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import  train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.svm import SVC\n",
    "%matplotlib inline\n",
    "iris = load_iris()\n",
    "# Take the first two features. We could avoid this by using a two-dim dataset\n",
    "X = iris.data[:, :2]\n",
    "y = iris.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T01:00:47.677501Z",
     "start_time": "2020-11-18T01:00:47.669151Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "clf = SVC(gamma='auto')\n",
    "clf.fit(X_train, y_train)\n",
    "print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-18T01:01:32.054637Z",
     "start_time": "2020-11-18T01:01:31.141307Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rchen/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10), dpi=144)\n",
    "\n",
    "clf_linear = svm.SVC(C=1.0, kernel='linear')\n",
    "clf_poly = svm.SVC(C=1.0, kernel='poly', degree=3)\n",
    "clf_rbf = svm.SVC(C=1.0, kernel='rbf', gamma=0.5)\n",
    "clf_rbf2 = svm.SVC(C=1.0, kernel='rbf', gamma=0.1)\n",
    "clfs = [clf_linear, clf_poly, clf_rbf, clf_rbf2]\n",
    "titles = ['Linear Kernel', \n",
    "          'Polynomial Kernel with Degree=3', \n",
    "          'Gaussian Kernel with $\\gamma=0.5$', \n",
    "          'Gaussian Kernel with $\\gamma=0.1$']\n",
    "for clf, i in zip(clfs, range(len(clfs))):\n",
    "    clf.fit(X, y)\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plot_hyperplane(clf, X, y, title=titles[i])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sklearn.svm.SVC\n",
    "\n",
    "*(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape=None,random_state=None)*\n",
    "\n",
    "参数：\n",
    "\n",
    "- C：C-SVC的惩罚参数C?默认值是1.0\n",
    "\n",
    "C越大，相当于惩罚松弛变量，希望松弛变量接近0，即对误分类的惩罚增大，趋向于对训练集全分对的情况，这样对训练集测试时准确率很高，但泛化能力弱。C值小，对误分类的惩罚减小，允许容错，将他们当成噪声点，泛化能力较强。\n",
    "\n",
    "- kernel ：核函数，默认是rbf，可以是‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ \n",
    "    \n",
    "    – 线性：u'v\n",
    "    \n",
    "    – 多项式：(gamma*u'*v + coef0)^degree\n",
    "\n",
    "    – RBF函数：exp(-gamma|u-v|^2)\n",
    "\n",
    "    – sigmoid：tanh(gamma*u'*v + coef0)\n",
    "\n",
    "\n",
    "- degree ：多项式poly函数的维度，默认是3，选择其他核函数时会被忽略。\n",
    "\n",
    "\n",
    "- gamma ： ‘rbf’,‘poly’ 和‘sigmoid’的核函数参数。默认是’auto’，则会选择1/n_features\n",
    "\n",
    "\n",
    "- coef0 ：核函数的常数项。对于‘poly’和 ‘sigmoid’有用。\n",
    "\n",
    "\n",
    "- probability ：是否采用概率估计？.默认为False\n",
    "\n",
    "\n",
    "- shrinking ：是否采用shrinking heuristic方法，默认为true\n",
    "\n",
    "\n",
    "- tol ：停止训练的误差值大小，默认为1e-3\n",
    "\n",
    "\n",
    "- cache_size ：核函数cache缓存大小，默认为200\n",
    "\n",
    "\n",
    "- class_weight ：类别的权重，字典形式传递。设置第几类的参数C为weight*C(C-SVC中的C)\n",
    "\n",
    "\n",
    "- verbose ：允许冗余输出？\n",
    "\n",
    "\n",
    "- max_iter ：最大迭代次数。-1为无限制。\n",
    "\n",
    "\n",
    "- decision_function_shape ：‘ovo’, ‘ovr’ or None, default=None3\n",
    "\n",
    "\n",
    "- random_state ：数据洗牌时的种子值，int值\n",
    "\n",
    "\n",
    "主要调节的参数有：C、kernel、degree、gamma、coef0。\n",
    "\n",
    "-----\n",
    "参考资料：\n",
    "\n",
    "[1] :[Lagrange Multiplier and KKT](http://blog.csdn.net/xianlingmao/article/details/7919597)\n",
    "\n",
    "[2] :[推导SVM](https://my.oschina.net/dfsj66011/blog/517766)\n",
    "\n",
    "[3] :[机器学习算法实践-支持向量机(SVM)算法原理](http://pytlab.org/2017/08/15/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E5%AE%9E%E8%B7%B5-%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA-SVM-%E7%AE%97%E6%B3%95%E5%8E%9F%E7%90%86/)\n",
    "\n",
    "[4] :[Python实现SVM](http://blog.csdn.net/wds2006sdo/article/details/53156589)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "参考代码：https://github.com/wzyonggege/statistical-learning-method\n",
    "\n",
    "中文注释制作：机器学习初学者\n",
    "\n",
    "微信公众号：ID:ai-start-com\n",
    "\n",
    "配置环境：python 3.5+\n",
    "\n",
    "代码全部测试通过。\n",
    "![gongzhong](images/gongzhong.jpg)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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